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Ting LH, Gick B, Kesar TM, Xu J. Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230485. [PMID: 39155720 DOI: 10.1098/rstb.2023.0485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 08/20/2024] Open
Abstract
Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
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Affiliation(s)
- Lena H Ting
- Coulter Department of Biomedical Engineering at Georgia Tech and Emory, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Bryan Gick
- Department of Linguistics, The University British Columbia, Vancouver, BC V6T 1Z4, Canada
- Haskins Laboratories, Yale University, New Haven, CT 06520, USA
| | - Trisha M Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, GA 30322, USA
| | - Jing Xu
- Department of Kinesiology, The University of Georgia, Athens, GA 30602, USA
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2
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Luis I, Afschrift M, De Groote F, Gutierrez-Farewik EM. Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds. PLoS Comput Biol 2024; 20:e1012411. [PMID: 39269982 DOI: 10.1371/journal.pcbi.1012411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 09/25/2024] [Accepted: 08/13/2024] [Indexed: 09/15/2024] Open
Abstract
The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online.
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Affiliation(s)
- Israel Luis
- KTH MoveAbility, Dept. Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Maarten Afschrift
- Faculty of Behavioural and Movement Sciences, VU Amsterdam, Amsterdam, The Netherlands
| | | | - Elena M Gutierrez-Farewik
- KTH MoveAbility, Dept. Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
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Kaufmann P, Koller W, Wallnöfer E, Goncalves B, Baca A, Kainz H. Increased trial-to-trial similarity and reduced temporal overlap of muscle synergy activation coefficients manifest during learning and with increasing movement proficiency. Sci Rep 2024; 14:17638. [PMID: 39085397 PMCID: PMC11291506 DOI: 10.1038/s41598-024-68515-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 07/23/2024] [Indexed: 08/02/2024] Open
Abstract
Muscle synergy analyses are used to enhance our understanding of motor control. Spatially fixed synergy weights coordinate multiple co-active muscles through activation commands, known as activation coefficients. To gain a more comprehensive understanding of motor learning, it is essential to understand how activation coefficients vary during a learning task and at different levels of movement proficiency. Participants walked on a line, a beam, and learned to walk on a tightrope-tasks that represent different levels of proficiency. Muscle synergies were extracted from electromyography signals across all conditions and the number of synergies was determined by the knee-point of the total variance accounted for (tVAF) curve. The results indicated that the tVAF of one synergy decreased with task proficiency, with the tightrope task resulting in the highest tVAF compared to the line and beam tasks. Furthermore, with increasing proficiency and after a learning process, trial-to-trial similarity increased and temporal overlap of synergy activation coefficients decreased. Consequently, we propose that precise adjustment and refinement of synergy activation coefficients play a pivotal role in motor learning.
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Affiliation(s)
- Paul Kaufmann
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Willi Koller
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Elias Wallnöfer
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Basilio Goncalves
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria
| | - Arnold Baca
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria
| | - Hans Kainz
- Department of Biomechanics, Kinesiology and Computer Science in Sport, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a (USZ ||), 1150, Vienna, Austria.
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Auf Der Schmelz 6a, 1150, Vienna, Austria.
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Buchmann A, Wenzler S, Welte L, Renjewski D. The effect of including a mobile arch, toe joint, and joint coupling on predictive neuromuscular simulations of human walking. Sci Rep 2024; 14:14879. [PMID: 38937584 PMCID: PMC11211509 DOI: 10.1038/s41598-024-65258-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 06/18/2024] [Indexed: 06/29/2024] Open
Abstract
Predictive neuromuscular simulations are a powerful tool for studying the biomechanics of human walking, and deriving design criteria for technical devices like prostheses or biorobots. Good agreement between simulation and human data is essential for transferability to the real world. The human foot is often modeled with a single rigid element, but knowledge of how the foot model affects gait prediction is limited. Standardized procedures for selecting appropriate foot models are lacking. We performed 2D predictive neuromuscular simulations with six different foot models of increasing complexity to answer two questions: What is the effect of a mobile arch, a toe joint, and the coupling of toe and arch motion through the plantar fascia on gait prediction? and How much of the foot's anatomy do we need to model to predict sagittal plane walking kinematics and kinetics in good agreement with human data? We found that the foot model had a significant impact on ankle kinematics during terminal stance, push-off, and toe and arch kinematics. When focusing only on hip and knee kinematics, rigid foot models are sufficient. We hope our findings will help guide the community in modeling the human foot according to specific research goals and improve neuromuscular simulation accuracy.
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Affiliation(s)
- Alexandra Buchmann
- Chair of Applied Mechanics, Technical University of Munich, 85748, Garching, Germany.
| | - Simon Wenzler
- Chair of Applied Mechanics, Technical University of Munich, 85748, Garching, Germany
| | - Lauren Welte
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Daniel Renjewski
- Chair of Applied Mechanics, Technical University of Munich, 85748, Garching, Germany
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Wechsler I, Wolf A, Shanbhag J, Leyendecker S, Eskofier BM, Koelewijn AD, Wartzack S, Miehling J. Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review. Front Bioeng Biotechnol 2024; 12:1386874. [PMID: 38919383 PMCID: PMC11196827 DOI: 10.3389/fbioe.2024.1386874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.
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Affiliation(s)
- Iris Wechsler
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alexander Wolf
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Julian Shanbhag
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sigrid Leyendecker
- Institute of Applied Dynamics, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anne D. Koelewijn
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sandro Wartzack
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jörg Miehling
- Engineering Design, Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Rabbi MF, Davico G, Lloyd DG, Carty CP, Diamond LE, Pizzolato C. Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy. Biomech Model Mechanobiol 2024; 23:1077-1090. [PMID: 38459157 PMCID: PMC11101562 DOI: 10.1007/s10237-024-01825-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/09/2024] [Indexed: 03/10/2024]
Abstract
Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment.
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Affiliation(s)
- Mohammad Fazle Rabbi
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Gold Coast, and Advanced Design and Prototyping Technologies Institute, Gold Coast, QLD, 4222, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Giorgio Davico
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, 40136, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Gold Coast, and Advanced Design and Prototyping Technologies Institute, Gold Coast, QLD, 4222, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Gold Coast, and Advanced Design and Prototyping Technologies Institute, Gold Coast, QLD, 4222, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, 4222, Australia
- Department of Orthopaedic Surgery, Children's Health Queensland Hospital and Health Service, Brisbane, QLD, 4101, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Gold Coast, and Advanced Design and Prototyping Technologies Institute, Gold Coast, QLD, 4222, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Gold Coast, and Advanced Design and Prototyping Technologies Institute, Gold Coast, QLD, 4222, Australia.
- School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, 4222, Australia.
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Xiao Z, Li C, Wang X, Guo J, Tian Q. Muscle Strength Identification Based on Isokinetic Testing and Spine Musculoskeletal Modeling. CYBORG AND BIONIC SYSTEMS 2024; 5:0113. [PMID: 39040710 PMCID: PMC11261815 DOI: 10.34133/cbsystems.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/15/2024] [Indexed: 07/24/2024] Open
Abstract
Subject-specific spinal musculoskeletal modeling can help understand the spinal loading mechanism during human locomotion. However, existing literature lacks methods to identify the maximum isometric strength of individual spinal muscles. In this study, a muscle strength identification method combining isokinetic testing and musculoskeletal simulations was proposed, and the influence of muscle synergy and intra-abdominal pressure (IAP) on identified spinal muscle strength was further discussed. A multibody dynamic model of the spinal musculoskeletal system was established and controlled by a feedback controller. Muscle strength parameters were adjusted based on the measured isokinetic moments, and muscle synergy vectors and the IAP piston model were further introduced. The results of five healthy subjects showed that the proposed method successfully identified the subject-specific spinal flexor/extensor strength. Considering the synergistic activations of antagonist muscles improved the correlation between the simulated and measured spinal moments, and the introduction of IAP slightly increased the identified spinal extensor strength. The established method is beneficial for understanding spinal loading distributions for athletes and patients with sarcopenia.
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Affiliation(s)
- Zuming Xiao
- MOE Key Laboratory of Dynamics and Control of Flight Vehicle, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
| | - Chang Li
- Professional and Technical Innovation Center for Exercise Diagnosis and Evaluation, Shenyang Sport University, Shenyang, China
| | - Xin Wang
- Professional and Technical Innovation Center for Exercise Diagnosis and Evaluation, Shenyang Sport University, Shenyang, China
| | - Jianqiao Guo
- MOE Key Laboratory of Dynamics and Control of Flight Vehicle, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
| | - Qiang Tian
- MOE Key Laboratory of Dynamics and Control of Flight Vehicle, School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China
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Li G, Ao D, Vega MM, Zandiyeh P, Chang SH, Penny AN, Lewis VO, Fregly BJ. Changes in walking function and neural control following pelvic cancer surgery with reconstruction. Front Bioeng Biotechnol 2024; 12:1389031. [PMID: 38827035 PMCID: PMC11140731 DOI: 10.3389/fbioe.2024.1389031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.
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Affiliation(s)
- Geng Li
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Payam Zandiyeh
- Biomotion Laboratory, Department of Orthopedic Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shuo-Hsiu Chang
- Department of Physical Medicine and Rehabilitation, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Alexander. N. Penny
- Department of Orthopedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Valerae O. Lewis
- Department of Orthopedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
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Di A, Benjamin JF. Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.03.583228. [PMID: 38496460 PMCID: PMC10942366 DOI: 10.1101/2024.03.03.583228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Background Calibrated electromyography (EMG)-driven musculoskeletal models can provide great insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unclear. Methods This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces. Results On average, SynX produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15 , r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N , r value 0.53 vs. 0.07) compared to SO. SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values. Conclusions These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.
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Affiliation(s)
- Ao Di
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - J Fregly Benjamin
- Department for Mechanical Engineering, Rice University, Houston, Texas, USA
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Cashaback JGA, Allen JL, Chou AHY, Lin DJ, Price MA, Secerovic NK, Song S, Zhang H, Miller HL. NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework. J Neuroeng Rehabil 2024; 21:23. [PMID: 38347597 PMCID: PMC10863253 DOI: 10.1186/s12984-024-01318-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Abstract
In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.
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Affiliation(s)
- Joshua G A Cashaback
- Biomedical Engineering, Mechanical Engineering, Kinesiology and Applied Physiology, Biome chanics and Movement Science Program, Interdisciplinary Neuroscience Graduate Program, University of Delaware, 540 S College Ave, Newark, DE, 19711, USA.
| | - Jessica L Allen
- Department of Mechanical Engineering, University of Florida, Gainesville, USA
| | | | - David J Lin
- Division of Neurocritical Care and Stroke Service, Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Providence, USA
| | - Mark A Price
- Department of Mechanical and Industrial Engineering, Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA
| | - Natalija K Secerovic
- School of Electrical Engineering, The Mihajlo Pupin Institute, University of Belgrade, Belgrade, Serbia
- Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems ETH Zürich, Zurich, Switzerland
| | - Seungmoon Song
- Mechanical and Industrial Engineering, Northeastern University, Boston, USA
| | - Haohan Zhang
- Department of Mechanical Engineering, University of Utah, Salt Lake City, USA
| | - Haylie L Miller
- School of Kinesiology, University of Michigan, 830 N University Ave, Ann Arbor, MI, 48109, USA.
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Winner TS, Rosenberg MC, Jain K, Kesar TM, Ting LH, Berman GJ. Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Comput Biol 2023; 19:e1011556. [PMID: 37889927 PMCID: PMC10610102 DOI: 10.1371/journal.pcbi.1011556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.
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Affiliation(s)
- Taniel S. Winner
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Michael C. Rosenberg
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Kanishk Jain
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Trisha M. Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, Georgia, United States of America
| | - Lena H. Ting
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, Georgia, United States of America
| | - Gordon J. Berman
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
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12
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Willson AM, Anderson AJ, Richburg CA, Muir BC, Czerniecki J, Steele KM, Aubin PM. Full body musculoskeletal model for simulations of gait in persons with transtibial amputation. Comput Methods Biomech Biomed Engin 2023; 26:412-423. [PMID: 35499924 PMCID: PMC9626388 DOI: 10.1080/10255842.2022.2065630] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
This paper describes the development, properties, and evaluation of a musculoskeletal model that reflects the anatomical and prosthetic properties of a transtibial amputee using OpenSim. Average passive prosthesis properties were used to develop CAD models of a socket, pylon, and foot to replace the lower leg. Additional degrees of freedom (DOF) were included in each joint of the prosthesis for potential use in a range of research areas, such as socket torque and socket pistoning. The ankle has three DOFs to provide further generality to the model. Seven transtibial amputee subjects were recruited for this study. 3 D motion capture, ground reaction force, and electromyographic (EMG) data were collected while participants wore their prescribed prosthesis, and then a passive prototype prosthesis instrumented with a 6-DOF load cell in series with the pylon. The model's estimates of the ankle, knee, and hip kinematics comparable to previous studies. The load cell provided an independent experimental measure of ankle joint torque, which was compared to inverse dynamics results from the model and showed a 7.7% mean absolute error. EMG data and muscle outputs from OpenSim's Static Optimization tool were qualitatively compared and showed reasonable agreement. Further improvements to the muscle characteristics or prosthesis-specific foot models may be necessary to better characterize individual amputee gait. The model is open-source and available at (https://simtk.org/projects/biartprosthesis) for other researchers to use to advance our understanding and amputee gait and assist with the development of new lower limb prostheses.
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Affiliation(s)
- Andrea M. Willson
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA,VA RR&D Center for Limb Loss and MoBility (CLiMB), Seattle WA, USA
| | - Anthony J. Anderson
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA,VA RR&D Center for Limb Loss and MoBility (CLiMB), Seattle WA, USA
| | | | - Brittney C. Muir
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA,VA RR&D Center for Limb Loss and MoBility (CLiMB), Seattle WA, USA
| | - Joseph Czerniecki
- VA RR&D Center for Limb Loss and MoBility (CLiMB), Seattle WA, USA,Department of Rehabilitation Medicine, University of Washington, Seattle WA, USA
| | - Katherine M. Steele
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA
| | - Patrick M. Aubin
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA,VA RR&D Center for Limb Loss and MoBility (CLiMB), Seattle WA, USA
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13
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Li G, Ao D, Vega MM, Shourijeh MS, Zandiyeh P, Chang SH, Lewis VO, Dunbar NJ, Babazadeh-Naseri A, Baines AJ, Fregly BJ. A computational method for estimating trunk muscle activations during gait using lower extremity muscle synergies. Front Bioeng Biotechnol 2022; 10:964359. [PMID: 36582837 PMCID: PMC9792665 DOI: 10.3389/fbioe.2022.964359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/21/2022] [Indexed: 12/15/2022] Open
Abstract
One of the surgical treatments for pelvic sarcoma is the restoration of hip function with a custom pelvic prosthesis after cancerous tumor removal. The orthopedic oncologist and orthopedic implant company must make numerous often subjective decisions regarding the design of the pelvic surgery and custom pelvic prosthesis. Using personalized musculoskeletal computer models to predict post-surgery walking function and custom pelvic prosthesis loading is an emerging method for making surgical and custom prosthesis design decisions in a more objective manner. Such predictions would necessitate the estimation of forces generated by muscles spanning the lower trunk and all joints of the lower extremities. However, estimating trunk and leg muscle forces simultaneously during walking based on electromyography (EMG) data remains challenging due to the limited number of EMG channels typically used for measurement of leg muscle activity. This study developed a computational method for estimating unmeasured trunk muscle activations during walking using lower extremity muscle synergies. To facilitate the calibration of an EMG-driven model and the estimation of leg muscle activations, EMG data were collected from each leg. Using non-negative matrix factorization, muscle synergies were extracted from activations of leg muscles. On the basis of previous studies, it was hypothesized that the time-varying synergy activations were shared between the trunk and leg muscles. The synergy weights required to reconstruct the trunk muscle activations were determined through optimization. The accuracy of the synergy-based method was dependent on the number of synergies and optimization formulation. With seven synergies and an increased level of activation minimization, the estimated activations of the erector spinae were strongly correlated with their measured activity. This study created a custom full-body model by combining two existing musculoskeletal models. The model was further modified and heavily personalized to represent various aspects of the pelvic sarcoma patient, all of which contributed to the estimation of trunk muscle activations. This proposed method can facilitate the prediction of post-surgery walking function and pelvic prosthesis loading, as well as provide objective evaluations for surgical and prosthesis design decisions.
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Affiliation(s)
- Geng Li
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Payam Zandiyeh
- Biomotion Laboratory, Department of Orthopaedic Surgery, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shuo-Hsiu Chang
- Department of Physical Medicine and Rehabilitation, McGovern Medical School at the University of Texas Health Science Center at Houston, Houston, TX, United States,Neurorecovery Research Center, TIRR Memorial Hermann, Houston, TX, United States
| | - Valerae O. Lewis
- Department of Orthopaedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Nicholas J. Dunbar
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Ata Babazadeh-Naseri
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Andrew J. Baines
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States,*Correspondence: Benjamin J. Fregly,
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14
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Vega MM, Li G, Shourijeh MS, Ao D, Weinschenk RC, Patten C, Font-Llagunes JM, Lewis VO, Fregly BJ. Computational evaluation of psoas muscle influence on walking function following internal hemipelvectomy with reconstruction. Front Bioeng Biotechnol 2022; 10:855870. [PMID: 36246391 PMCID: PMC9559731 DOI: 10.3389/fbioe.2022.855870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient's post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.
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Affiliation(s)
- Marleny M. Vega
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Geng Li
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Robert C. Weinschenk
- Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, UC Davis School of Medicine, Sacramento, CA, United States
- UC Davis Center for Neuroengineering and Medicine, University of California, Davis, CA, United States
- VA Northern California Health Care System, Martinez, CA, United States
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Valerae O. Lewis
- Department of Orthopaedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
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15
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Haralabidis N, Colyer SL, Serrancolí G, Salo AIT, Cazzola D. Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study. Sci Rep 2022; 12:15908. [PMID: 36151260 PMCID: PMC9508344 DOI: 10.1038/s41598-022-20023-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 09/07/2022] [Indexed: 11/09/2022] Open
Abstract
The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left 'free' to change. It was found that the 'knee-free' and 'knee-hip-free' simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.
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Affiliation(s)
- Nicos Haralabidis
- Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK. .,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, UK. .,Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - Steffi L Colyer
- Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, UK
| | - Gil Serrancolí
- Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Aki I T Salo
- Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, UK.,KIHU Finnish Institute of High Performance Sport, Jyväskylä, Finland
| | - Dario Cazzola
- Department for Health, University of Bath, Claverton Down, Bath, BA2 7AY, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, UK
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16
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Ao D, Vega MM, Shourijeh MS, Patten C, Fregly BJ. EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation. Front Bioeng Biotechnol 2022; 10:962959. [PMID: 36159690 PMCID: PMC9490010 DOI: 10.3389/fbioe.2022.962959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called "synergy extrapolation" or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were "unmeasured" for walking datasets collected from two individuals post-stroke-one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.
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Affiliation(s)
- Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Marleny M. Vega
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, VA Northern California Health Care System, Martinez, CA, United States
- Department of Physical Medicine and Rehabilitation, Davis School of Medicine, University of California, Sacramento, CA, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
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17
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Wochner I, Nölle LV, Martynenko OV, Schmitt S. ‘Falling heads’: investigating reflexive responses to head–neck perturbations. Biomed Eng Online 2022; 21:25. [PMID: 35429975 PMCID: PMC9013062 DOI: 10.1186/s12938-022-00994-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/29/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Reflexive responses to head–neck perturbations affect the injury risk in many different situations ranging from sports-related impact to car accident scenarios. Although several experiments have been conducted to investigate these head–neck responses to various perturbations, it is still unclear why and how individuals react differently and what the implications of these different responses across subjects on the potential injuries might be. Therefore, we see a need for both experimental data and biophysically valid computational Human Body Models with bio-inspired muscle control strategies to understand individual reflex responses better.
Methods
To address this issue, we conducted perturbation experiments of the head–neck complex and used this data to examine control strategies in a simulation model. In the experiments, which we call ’falling heads’ experiments, volunteers were placed in a supine and a prone position on a table with an additional trapdoor supporting the head. This trapdoor was suddenly released, leading to a free-fall movement of the head until reflexive responses of muscles stopped the downwards movement.
Results
We analysed the kinematic, neuronal and dynamic responses for all individuals and show their differences for separate age and sex groups. We show that these results can be used to validate two simple reflex controllers which are able to predict human biophysical movement and modulate the response necessary to represent a large variability of participants.
Conclusions
We present characteristic parameters such as joint stiffness, peak accelerations and latency times. Based on this data, we show that there is a large difference in the individual reflexive responses between participants. Furthermore, we show that the perturbation direction (supine vs. prone) significantly influences the measured kinematic quantities. Finally, ’falling heads’ experiments data are provided open-source to be used as a benchmark test to compare different muscle control strategies and to validate existing active Human Body Models directly.
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18
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Kuska EC, Mehrabi N, Schwartz MH, Steele KM. Number of synergies impacts sensitivity of gait to weakness and contracture. J Biomech 2022; 134:111012. [PMID: 35219146 PMCID: PMC8976766 DOI: 10.1016/j.jbiomech.2022.111012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 11/17/2022]
Abstract
Muscle activity during gait can be described by a small set of synergies, weighted groups of muscles, that are theorized to reflect underlying neural control. For people with neurologic injuries, like cerebral palsy or stroke, even fewer synergies are required to explain muscle activity during gait. This reduction in synergies is thought to reflect altered control and is associated with impairment severity and treatment outcomes. Individuals with neurologic injuries also develop secondary musculoskeletal impairments, like weakness or contracture, that can impact gait. Yet, the combined impacts of altered control and musculoskeletal impairments on gait remains unclear. In this study, we use a two-dimensional musculoskeletal model constrained to synergy control to simulate unimpaired gait. We vary the number of synergies, while simulating muscle weakness and contracture to examine how altered control impacts sensitivity to musculoskeletal impairment while tracking unimpaired gait. Results demonstrate that reducing the number of synergies increases sensitivity to weakness and contracture for specific muscle groups. For example, simulations using five-synergy control tolerated 40% and 51% more knee extensor weakness than those using four- or three-synergy control, respectively. Furthermore, when constrained to four- or three-synergy control, the model was increasingly sensitive to contracture and weakness of proximal muscles, such as the hamstring and hip flexors. Contrastingly, neither the amount of generalized nor plantarflexor weakness tolerated was affected by the number of synergies. These findings highlight the interactions between altered control and musculoskeletal impairments, emphasizing the importance of measuring and incorporating both in future simulation and experimental studies.
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Affiliation(s)
- Elijah C Kuska
- Department of Mechanical Engineering, University of Washington, Seattle, WA, United States.
| | - Naser Mehrabi
- Department of Mechanical Engineering, University of Washington, Seattle, WA, United States
| | - Michael H Schwartz
- Center for Gait & Motional Analysis, Gillette Children's Specialty Healthcare, St. Paul, MN, United States
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, United States
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19
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Febrer-Nafría M, Fregly BJ, Font-Llagunes JM. Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury. Front Neurorobot 2022; 15:748148. [PMID: 35140596 PMCID: PMC8818856 DOI: 10.3389/fnbot.2021.748148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.
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Affiliation(s)
- Míriam Febrer-Nafría
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | - Benjamin J Fregly
- Deptartment of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Josep M Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
- Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
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20
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Zhao J, Yu Y, Wang X, Ma S, Sheng X, Zhu X. A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements. J Neural Eng 2022; 19. [PMID: 34986472 DOI: 10.1088/1741-2552/ac4851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. APPROACH Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. MAIN RESULTS The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. SIGNIFICANCE The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.
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Affiliation(s)
- Jiamin Zhao
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Yang Yu
- Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan RD. Minhang District, Shanghai, 200240, CHINA
| | - Xu Wang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Shihan Ma
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Xinjun Sheng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
| | - Xiangyang Zhu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, China, Shanghai, 200240, CHINA
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21
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Santos GF, Jakubowitz E, Pronost N, Bonis T, Hurschler C. Predictive simulation of post-stroke gait with functional electrical stimulation. Sci Rep 2021; 11:21351. [PMID: 34725376 PMCID: PMC8560756 DOI: 10.1038/s41598-021-00658-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/14/2021] [Indexed: 11/09/2022] Open
Abstract
Post-stroke patients present various gait abnormalities such as drop foot, stiff-knee gait (SKG), and knee hyperextension. Functional electrical stimulation (FES) improves drop foot gait although the mechanistic basis for this effect is not well understood. To answer this question, we evaluated the gait of a post-stroke patient walking with and without FES by inverse dynamics analysis and compared the results to an optimal control framework. The effect of FES and cause-effect relationship of changes in knee and ankle muscle strength were investigated; personalized muscle–tendon parameters allowed the prediction of pathologic gait. We also predicted healthy gait patterns at different speeds to simulate the subject walking without impairment. The passive moment of the knee played an important role in the estimation of muscle force with knee hyperextension, which was decreased during FES and knee extensor strengthening. Weakening the knee extensors and strengthening the flexors improved SKG. During FES, weak ankle plantarflexors and strong ankle dorsiflexors resulted in increased ankle dorsiflexion, which reduced drop foot. FES also improved gait speed and reduced circumduction. These findings provide insight into compensatory strategies adopted by post-stroke patients that can guide the design of individualized rehabilitation and treatment programs.
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Affiliation(s)
- Gilmar F Santos
- Laboratory for Biomechanics and Biomaterials, Department of Orthopedics, Hannover Medical School, Hannover, Germany.
| | - Eike Jakubowitz
- Laboratory for Biomechanics and Biomaterials, Department of Orthopedics, Hannover Medical School, Hannover, Germany
| | - Nicolas Pronost
- CNRS LIRIS, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Thomas Bonis
- CNRS LIRIS, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Christof Hurschler
- Laboratory for Biomechanics and Biomaterials, Department of Orthopedics, Hannover Medical School, Hannover, Germany
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22
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Saito H, Yokoyama H, Sasaki A, Kato T, Nakazawa K. Flexible Recruitments of Fundamental Muscle Synergies in the Trunk and Lower Limbs for Highly Variable Movements and Postures. SENSORS (BASEL, SWITZERLAND) 2021; 21:6186. [PMID: 34577394 PMCID: PMC8472977 DOI: 10.3390/s21186186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/08/2021] [Accepted: 09/13/2021] [Indexed: 11/16/2022]
Abstract
The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and lower limb muscles, during 24 locomotion, dynamic and static stability tasks, and we extracted the muscle synergies using non-negative matrix factorization. Our results show that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies are required to validate the neural representation of the fundamental components of muscle synergies.
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Affiliation(s)
- Hiroki Saito
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan; (H.S.); (H.Y.); (A.S.); (T.K.)
- Department of Physical Therapy, Tokyo University of Technology, Ota, Tokyo 144-8535, Japan
| | - Hikaru Yokoyama
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan; (H.S.); (H.Y.); (A.S.); (T.K.)
| | - Atsushi Sasaki
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan; (H.S.); (H.Y.); (A.S.); (T.K.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda, Tokyo 102-0083, Japan
| | - Tatsuya Kato
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan; (H.S.); (H.Y.); (A.S.); (T.K.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda, Tokyo 102-0083, Japan
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan; (H.S.); (H.Y.); (A.S.); (T.K.)
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23
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Myoelectric control and neuromusculoskeletal modeling: Complementary technologies for rehabilitation robotics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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24
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Hong YNG, Ballekere AN, Fregly BJ, Roh J. Are muscle synergies useful for stroke rehabilitation? CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100315] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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25
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Haralabidis N, Serrancolí G, Colyer S, Bezodis I, Salo A, Cazzola D. Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach. PeerJ 2021; 9:e10975. [PMID: 33732550 PMCID: PMC7950206 DOI: 10.7717/peerj.10975] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/29/2021] [Indexed: 11/20/2022] Open
Abstract
Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.
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Affiliation(s)
- Nicos Haralabidis
- Department for Health, University of Bath, Bath, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, Bath, UK
| | - Gil Serrancolí
- Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Steffi Colyer
- Department for Health, University of Bath, Bath, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, Bath, UK
| | - Ian Bezodis
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Aki Salo
- Department for Health, University of Bath, Bath, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, Bath, UK.,KIHU Research Institute for Olympic Sports, Jyväskylä, Finland
| | - Dario Cazzola
- Department for Health, University of Bath, Bath, UK.,CAMERA-Centre for the Analysis of Motion, Entertainment Research and Applications, Bath, UK
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26
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De Groote F, Falisse A. Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait. Proc Biol Sci 2021; 288:20202432. [PMID: 33653141 PMCID: PMC7935082 DOI: 10.1098/rspb.2020.2432] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/02/2021] [Indexed: 01/10/2023] Open
Abstract
Locomotion results from complex interactions between the central nervous system and the musculoskeletal system with its many degrees of freedom and muscles. Gaining insight into how the properties of each subsystem shape human gait is challenging as experimental methods to manipulate and assess isolated subsystems are limited. Simulations that predict movement patterns based on a mathematical model of the neuro-musculoskeletal system without relying on experimental data can reveal principles of locomotion by elucidating cause-effect relationships. New computational approaches have enabled the use of such predictive simulations with complex neuro-musculoskeletal models. Here, we review recent advances in predictive simulations of human movement and how those simulations have been used to deepen our knowledge about the neuromechanics of gait. In addition, we give a perspective on challenges towards using predictive simulations to gain new fundamental insight into motor control of gait, and to help design personalized treatments in patients with neurological disorders and assistive devices that improve gait performance. Such applications will require more detailed neuro-musculoskeletal models and simulation approaches that take uncertainty into account, tools to efficiently personalize those models, and validation studies to demonstrate the ability of simulations to predict gait in novel circumstances.
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Affiliation(s)
- Friedl De Groote
- Department of Movement Sciences, KU Leuven, Leuven, Flanders, Belgium
| | - Antoine Falisse
- Department of Movement Sciences, KU Leuven, Leuven, Flanders, Belgium
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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27
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A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11052037] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.
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28
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Michaud F, Lamas M, Lugrís U, Cuadrado J. A fair and EMG-validated comparison of recruitment criteria, musculotendon models and muscle coordination strategies, for the inverse-dynamics based optimization of muscle forces during gait. J Neuroeng Rehabil 2021; 18:17. [PMID: 33509205 PMCID: PMC7841909 DOI: 10.1186/s12984-021-00806-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/11/2021] [Indexed: 11/15/2022] Open
Abstract
Experimental studies and EMG collections suggest that a specific strategy of muscle coordination is chosen by the central nervous system to perform a given motor task. A popular mathematical approach for solving the muscle recruitment problem is optimization. Optimization-based methods minimize or maximize some criterion (objective function or cost function) which reflects the mechanism used by the central nervous system to recruit muscles for the movement considered. The proper cost function is not known a priori, so the adequacy of the chosen function must be validated according to the obtained results. In addition of the many criteria proposed, several physiological representations of the musculotendon actuator dynamics (that prescribe constraints for the forces) along with different musculoskeletal models can be found in the literature, which hinders the selection of the best neuromusculotendon model for each application. Seeking to provide a fair base for comparison, this study measures the efficiency and accuracy of: (i) four different criteria within the static optimization approach (where the physiological character of the muscle, which affects the constraints of the forces, is not considered); (ii) three physiological representations of the musculotendon actuator dynamics: activation dynamics with elastic tendon, simplified activation dynamics with rigid tendon and rigid tendon without activation dynamics; (iii) a synergy-based method; all of them within the framework of inverse-dynamics based optimization. Motion/force/EMG gait analyses were performed on ten healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, musculotendon kinematics and moment arms. Muscle activations were then estimated using the different approaches, and these estimates were compared with EMG measurements. Although no significant differences were obtained with all the methods at statistical level, it must be pointed out that a higher complexity of the method does not guarantee better results, as the best correlations with experimental values were obtained with two simplified approaches: the static optimization and the physiological approach with simplified activation dynamics and rigid tendon, both using the sum of the squares of muscle forces as objective function.
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Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain.
| | - Mario Lamas
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
| | - Urbano Lugrís
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
| | - Javier Cuadrado
- Laboratory of Mechanical Engineering, University of La Coruña, Ferrol, Spain
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29
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Ao D, Shourijeh MS, Patten C, Fregly BJ. Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies. Front Comput Neurosci 2020; 14:588943. [PMID: 33343322 PMCID: PMC7746870 DOI: 10.3389/fncom.2020.588943] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/09/2020] [Indexed: 12/14/2022] Open
Abstract
Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called "synergy extrapolation" or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.
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Affiliation(s)
- Di Ao
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Mohammad S. Shourijeh
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | - Carolynn Patten
- Biomechanics, Rehabilitation, and Integrative Neuroscience (BRaIN) Lab, VA Northern California Health Care System, Martinez, CA, United States
- Department of Physical Medicine and Rehabilitation, Davis School of Medicine, University of California, Sacramento, CA, United States
| | - Benjamin J. Fregly
- Rice Computational Neuromechanics Lab, Department of Mechanical Engineering, Rice University, Houston, TX, United States
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30
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Dembia CL, Bianco NA, Falisse A, Hicks JL, Delp SL. OpenSim Moco: Musculoskeletal optimal control. PLoS Comput Biol 2020; 16:e1008493. [PMID: 33370252 PMCID: PMC7793308 DOI: 10.1371/journal.pcbi.1008493] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 01/08/2021] [Accepted: 11/05/2020] [Indexed: 11/18/2022] Open
Abstract
Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.
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Affiliation(s)
- Christopher L. Dembia
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Nicholas A. Bianco
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Antoine Falisse
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Jennifer L. Hicks
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Scott L. Delp
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, United States of America
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31
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Arones MM, Shourijeh MS, Patten C, Fregly BJ. Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking. Front Bioeng Biotechnol 2020; 8:588925. [PMID: 33324623 PMCID: PMC7725798 DOI: 10.3389/fbioe.2020.588925] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022] Open
Abstract
Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject's CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.
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Affiliation(s)
- Marleny M. Arones
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
| | | | - Carolynn Patten
- Department of Physical Medicine and Rehabilitation, University of California, Davis, Davis, CA, United States
| | - Benjamin J. Fregly
- Department of Mechanical Engineering, Rice University, Houston, TX, United States
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32
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Al Borno M, Hicks JL, Delp SL. The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis. J R Soc Interface 2020; 17:20200011. [PMID: 32486950 PMCID: PMC7328389 DOI: 10.1098/rsif.2020.0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 05/06/2020] [Indexed: 11/12/2022] Open
Abstract
It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternative initial poses-with average errors similar to a full-dimensional controller.
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Affiliation(s)
- Mazen Al Borno
- Department of Bioengineering and Mechanical Engineering, Stanford University, Stanford, CA, USA
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33
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Costa-Garcia A, Ianez E, Sonoo M, Okajima S, Yamasaki H, Ueda S, Shimoda S. Segmentation and Averaging of sEMG Muscle Activations Prior to Synergy Extraction. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.2975729] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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34
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Pitto L, van Rossom S, Desloovere K, Molenaers G, Huenaerts C, De Groote F, Jonkers I. Pre-treatment EMG can be used to model post-treatment muscle coordination during walking in children with cerebral palsy. PLoS One 2020; 15:e0228851. [PMID: 32050002 PMCID: PMC7015404 DOI: 10.1371/journal.pone.0228851] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 01/25/2020] [Indexed: 12/19/2022] Open
Abstract
When treating children with Cerebral Palsy (CP), computational simulations based on musculoskeletal models have a great potential in assisting the clinical decision-making process towards the most promising treatments. In particular, predictive simulations could be used to predict and compare the functional outcome of a series of candidate interventions. In order to be able to benefit from these predictive simulations however, it is important to know how much information about the post-treatment patient’s motor control could be gathered from data available before the intervention. Within this paper, we quantified how much of the muscle activity measured after a treatment could be explained by subject-specific muscle synergies computed from EMG data collected before the intervention. We also investigated whether generic synergies could be used, in case no EMG data is available when running predictive simulations, to reproduce both pre- and post-treatment muscle activity in children with CP. Subject-specific synergies proved to be a good indicator of the patient’s post-treatment motor control, explaining on average more than 85% of the post-treatment muscle activity, compared to an average of 94% when applied to the original pre-treatment data. Generic synergies explained 84% of the pre-treatment and 83% of the post-treatment muscle activity on average, but performed relatively well for patients with low selective motor control and poorly in patients with more selectivity. Our results suggest that subject-specific muscle synergies computed from pre-treatment EMG data could be used with confidence to represent the post-treatment motor control of children with CP during walking. In addition, when performing simulations involving patients with a low selective motor control, generic synergies could be a valid alternative.
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Affiliation(s)
- Lorenzo Pitto
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
- * E-mail:
| | - Sam van Rossom
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Kaat Desloovere
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Clinical Motion Analysis Laboratory, University Hospital Leuven, Leuven, Belgium
| | - Guy Molenaers
- Department of Development and Regeneration, University Hospital Leuven, Leuven, Belgium
| | - Catherine Huenaerts
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Clinical Motion Analysis Laboratory, University Hospital Leuven, Leuven, Belgium
| | | | - Ilse Jonkers
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
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Shuman BR, Goudriaan M, Desloovere K, Schwartz MH, Steele KM. Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy. Front Neurorobot 2019; 13:102. [PMID: 31920612 PMCID: PMC6927914 DOI: 10.3389/fnbot.2019.00102] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 11/25/2019] [Indexed: 01/02/2023] Open
Abstract
Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control. The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data. We evaluated modeled muscle activations during gait for six typically developing (TD) children and six children with CP. Muscle activations were estimated with: (1) static optimization (SO), minimizing muscle activations squared, and (2) synergy SO (SynSO), minimizing synergy activations squared using the weights identified from EMG data for two to five synergies. While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups. The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for five synergies). Constraining activations to SynSO caused the simulated muscle stress to increase compared to SO for all individuals, causing a 157% increase with two synergies. These results suggest that constraining simulated activations in inverse dynamic simulations to subject-specific synergies alone may not improve estimation of muscle activations during gait for generic musculoskeletal models.
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Affiliation(s)
- Benjamin R. Shuman
- Department of Mechanical Engineering, University of Washington, Seattle, WA, United States
| | - Marije Goudriaan
- Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Kaat Desloovere
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Clinical Motion Analysis Laboratory, University Hospitals Leuven (Pellenberg), Lubbeek, Belgium
| | - Michael H. Schwartz
- James R. Gage Center for Gait and Motion Analysis, Gillette Children’s Specialty Healthcare, Saint Paul, MN, United States
- Orthopaedic Surgery, Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Katherine M. Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, United States
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36
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Falisse A, Serrancolí G, Dembia CL, Gillis J, De Groote F. Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement. PLoS One 2019; 14:e0217730. [PMID: 31622352 PMCID: PMC6797126 DOI: 10.1371/journal.pone.0217730] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 09/19/2019] [Indexed: 11/26/2022] Open
Abstract
Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of trajectory optimization problems as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of trajectory optimization of human movement, which will facilitate their use for biomechanical applications requiring the use of detailed models of the musculoskeletal system.
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Affiliation(s)
- Antoine Falisse
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
- * E-mail:
| | - Gil Serrancolí
- Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Catalunya, Spain
| | - Christopher L. Dembia
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Joris Gillis
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
- DMMS Lab, Flanders Make, Leuven, Belgium
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Sauder NR, Meyer AJ, Allen JL, Ting LH, Kesar TM, Fregly BJ. Computational Design of FastFES Treatment to Improve Propulsive Force Symmetry During Post-stroke Gait: A Feasibility Study. Front Neurorobot 2019; 13:80. [PMID: 31632261 PMCID: PMC6779709 DOI: 10.3389/fnbot.2019.00080] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 09/10/2019] [Indexed: 12/20/2022] Open
Abstract
Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient's gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient's unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.
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Affiliation(s)
- Nathan R Sauder
- Computational Biomechanics Laboratory, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, United States
| | - Andrew J Meyer
- Computational Biomechanics Laboratory, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, United States
| | - Jessica L Allen
- Neuromechanics Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
| | - Lena H Ting
- Neuromechanics Laboratory, Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States.,Motion Analysis Laboratory, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Trisha M Kesar
- Motion Analysis Laboratory, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Benjamin J Fregly
- Rice Computational Neuromechanics Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX, United States
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Pitto L, Kainz H, Falisse A, Wesseling M, Van Rossom S, Hoang H, Papageorgiou E, Hallemans A, Desloovere K, Molenaers G, Van Campenhout A, De Groote F, Jonkers I. SimCP: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy. Front Neurorobot 2019; 13:54. [PMID: 31379550 PMCID: PMC6650580 DOI: 10.3389/fnbot.2019.00054] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 07/03/2019] [Indexed: 12/19/2022] Open
Abstract
Gait deficits in cerebral palsy (CP) are often treated with a single-event multi-level surgery (SEMLS). Selecting the treatment options (combination of bony and soft tissue corrections) for a specific patient is a complex endeavor and very often treatment outcome is not satisfying. A deterioration in 22.8% of the parameters describing gait performance has been reported and there is need for additional surgery in 11% of the patients. Computational simulations based on musculoskeletal models that allow clinicians to test the effects of different treatment options before surgery have the potential to drastically improve treatment outcome. However, to date, no such simulation and modeling method is available. Two important challenges are the development of methods to include patient-specific neuromechanical impairments into the models and to simulate the effect of different surgical procedures on post-operative gait performance. Therefore, we developed the SimCP framework that allows the evaluation of the effect of different simulated surgeries on gait performance of a specific patient and includes a graphical user interface (GUI) that enables performing virtual surgery on the models. We demonstrated the potential of our framework for two case studies. Models reflecting the patient-specific musculoskeletal geometry and muscle properties are generated based solely on data collected before the treatment. The patient's motor control is described based on muscle synergies derived from pre-operative EMG. The GUI is then used to modify the musculoskeletal properties according to the surgical plan. Since SEMLS does not affect motor control, the same motor control model is used to define gait performance pre- and post-operative. We use the capability gap (CG), i.e., the difference between the joint moments needed to perform healthy walking and the joint moments the personalized model can generate, to quantify gait performance. In both cases, the CG was smaller post- then pre-operative and this was in accordance with the measured change in gait kinematics after treatment.
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Affiliation(s)
- Lorenzo Pitto
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Hans Kainz
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | | | | | - Sam Van Rossom
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Hoa Hoang
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Eirini Papageorgiou
- Department of Rehabilitation Sciences, Doctoral School of Biomedical Sciences, KU Leuven, Leuven, Belgium.,Clinical Motion Analysis Laboratory, University Hospitals Leuven, Leuven, Belgium
| | - Ann Hallemans
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
| | - Kaat Desloovere
- Department of Rehabilitation Sciences, Doctoral School of Biomedical Sciences, KU Leuven, Leuven, Belgium.,Clinical Motion Analysis Laboratory, University Hospitals Leuven, Leuven, Belgium
| | - Guy Molenaers
- Department of Orthopedics, University Hospitals Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Anja Van Campenhout
- Department of Orthopedics, University Hospitals Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | | | - Ilse Jonkers
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
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Mehrabi N, Schwartz MH, Steele KM. Can altered muscle synergies control unimpaired gait? J Biomech 2019; 90:84-91. [PMID: 31101431 DOI: 10.1016/j.jbiomech.2019.04.038] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 04/26/2019] [Accepted: 04/26/2019] [Indexed: 01/26/2023]
Abstract
Recent studies have postulated that the human motor control system recruits groups of muscles through low-dimensional motor commands, or muscle synergies. This scheme simplifies the neural control problem associated with the high-dimensional structure of the neuromuscular system. Several lines of evidence have suggested that neurological injuries, such as stroke or cerebral palsy, may reduce the dimensions that are available to the motor control system, and these altered dimensions or synergies are thought to contribute to impaired walking patterns. However, no study has investigated whether impaired low-dimensional control spaces necessarily lead to impaired walking patterns. In this study, using a two-dimensional model of walking, we developed a synergy-based control framework that can simulate the dynamics of walking. The simulation analysis showed that a synergy-based control scheme can produce well-coordinated movements of walking matching unimpaired gait. However, when the dimensions available to the controller were reduced, the simplified emergent pattern deviated from unimpaired gait. A system with two synergies, similar to those seen after neurological injury, could not produce an unimpaired walking pattern. These findings provide further evidence that altered muscle synergies can contribute to impaired gait patterns and may need to be directly addressed to improve gait after neurological injury.
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Affiliation(s)
- Naser Mehrabi
- Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Michael H Schwartz
- Gillette's Children Specialty Healthcare, Saint Paul, MN, USA; University of Minnesota, Minneapolis, MN, USA
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40
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Sharif Razavian R, Ghannadi B, McPhee J. On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems. Front Comput Neurosci 2019; 13:23. [PMID: 31040776 PMCID: PMC6477041 DOI: 10.3389/fncom.2019.00023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 03/29/2019] [Indexed: 11/20/2022] Open
Abstract
It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom. Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model. Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces. Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces. Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.
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Affiliation(s)
- Reza Sharif Razavian
- Motion Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
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41
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Sharif Razavian R, Ghannadi B, McPhee J. A Synergy-Based Motor Control Framework for the Fast Feedback Control of Musculoskeletal Systems. J Biomech Eng 2019; 141:2718207. [PMID: 30516245 DOI: 10.1115/1.4042185] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Indexed: 11/08/2022]
Abstract
This paper presents a computational framework for the fast feedback control of musculoskeletal systems using muscle synergies. The proposed motor control framework has a hierarchical structure. A feedback controller at the higher level of hierarchy handles the trajectory planning and error compensation in the task space. This high-level task space controller only deals with the task-related kinematic variables, and thus is computationally efficient. The output of the task space controller is a force vector in the task space, which is fed to the low-level controller to be translated into muscle activity commands. Muscle synergies are employed to make this force-to-activation (F2A) mapping computationally efficient. The explicit relationship between the muscle synergies and task space forces allows for the fast estimation of muscle activations that result in the reference force. The synergy-enabled F2A mapping replaces a computationally heavy nonlinear optimization process by a vector decomposition problem that is solvable in real time. The estimation performance of the F2A mapping is evaluated by comparing the F2A-estimated muscle activities against the measured electromyography (EMG) data. The results show that the F2A algorithm can estimate the muscle activations using only the task-related kinematics/dynamics information with ∼70% accuracy. An example predictive simulation is also presented, and the results show that this feedback motor control framework can control arbitrary movements of a three-dimensional (3D) musculoskeletal arm model quickly and near optimally. It is two orders-of-magnitude faster than the optimal controller, with only 12% increase in muscle activities compared to the optimal. The developed motor control model can be used for real-time near-optimal predictive control of musculoskeletal system dynamics.
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Affiliation(s)
- Reza Sharif Razavian
- Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada e-mail:
| | - Borna Ghannadi
- Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada e-mail:
| | - John McPhee
- Fellow ASME Professor Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada e-mail:
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42
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Santuz A, Ekizos A, Janshen L, Mersmann F, Bohm S, Baltzopoulos V, Arampatzis A. Modular Control of Human Movement During Running: An Open Access Data Set. Front Physiol 2018; 9:1509. [PMID: 30420812 PMCID: PMC6216155 DOI: 10.3389/fphys.2018.01509] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 10/08/2018] [Indexed: 12/31/2022] Open
Abstract
The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the 20th century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of 5 to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males and 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization (NMF) and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using NMF. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing, and late swing. We foresee many possible applications of our data that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modeling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. All the data is available at Zenodo (doi: 10.5281/zenodo.1254380).
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Affiliation(s)
- Alessandro Santuz
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Antonis Ekizos
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lars Janshen
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Falk Mersmann
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Bohm
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vasilios Baltzopoulos
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Adamantios Arampatzis
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
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43
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Duysens J, Forner-Cordero A. Walking with perturbations: a guide for biped humans and robots. BIOINSPIRATION & BIOMIMETICS 2018; 13:061001. [PMID: 30109860 DOI: 10.1088/1748-3190/aada54] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper provides an update on the neural control of bipedal walking in relation to bioinspired models and robots. It is argued that most current models or robots are based on the construct of a symmetrical central pattern generator (CPG). However, new evidence suggests that CPG functioning is basically asymmetrical with its flexor half linked more tightly to the rhythm generator. The stability of bipedal gait, which is an important problem for robots and biological systems, is also addressed. While it is not possible to determine how biological biped systems guarantee stability, robot solutions can be useful to propose new hypotheses for biology. In the second part of this review, the focus is on gait perturbations, which is an important topic in robotics in view of the frequent falls of robots when faced with perturbations. From the human physiology it is known that the initial reaction often consists of a brief interruption followed by an adequate response. For instance, the successful recovery from a trip is achieved using some basic reactions (termed elevating and lowering strategies), that depend on the phase of the step cycle of the trip occurrence. Reactions to stepping unexpectedly in a hole depend on comparing expected and real feedback. Implementation of these ideas in models and robotics starts to emerge, with the most advanced robots being able to learn how to fall safely and how to deal with complicated disturbances such as provided by walking on a split-belt.
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Affiliation(s)
- Jacques Duysens
- Biomechatronics Lab., Mechatronics Department, Escola Politécnica da Universidade de São Paulo, Av. Prof. Mello Moraes, 2231, Cidade Universitária 05508-030, São Paulo-SP, Brasil. Department of Kinesiology, FaBeR, Katholieke Universiteit Leuven, Leuven, Belgium
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44
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Seth A, Hicks JL, Uchida TK, Habib A, Dembia CL, Dunne JJ, Ong CF, DeMers MS, Rajagopal A, Millard M, Hamner SR, Arnold EM, Yong JR, Lakshmikanth SK, Sherman MA, Ku JP, Delp SL. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 2018; 14:e1006223. [PMID: 30048444 PMCID: PMC6061994 DOI: 10.1371/journal.pcbi.1006223] [Citation(s) in RCA: 401] [Impact Index Per Article: 66.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 05/23/2018] [Indexed: 11/19/2022] Open
Abstract
Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human-device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim's design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
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Affiliation(s)
- Ajay Seth
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Jennifer L. Hicks
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Thomas K. Uchida
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Ayman Habib
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Christopher L. Dembia
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - James J. Dunne
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Carmichael F. Ong
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Matthew S. DeMers
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Apoorva Rajagopal
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Matthew Millard
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Samuel R. Hamner
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Edith M. Arnold
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | - Jennifer R. Yong
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
| | | | - Michael A. Sherman
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Joy P. Ku
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Scott L. Delp
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, United States of America
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45
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Bianco NA, Patten C, Fregly BJ. Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations? J Biomech Eng 2018; 140:2658262. [PMID: 29049521 DOI: 10.1115/1.4038199] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Indexed: 11/08/2022]
Abstract
Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called "muscle excitations"), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called "included" muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called "excluded" muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called "synergy extrapolation"). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.
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Affiliation(s)
- Nicholas A Bianco
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305
| | - Carolynn Patten
- Neural Control of Movement Lab, Malcom Randall VA Medical Center and Department of Physical Therapy, University of Florida, Gainesville, FL 32610
| | - Benjamin J Fregly
- Department of Mechanical Engineering, Rice University, 6100 Main Street, P.O. Box 1892, Houston, TX 77251-1892 e-mail:
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Lin YC, Walter JP, Pandy MG. Predictive Simulations of Neuromuscular Coordination and Joint-Contact Loading in Human Gait. Ann Biomed Eng 2018; 46:1216-1227. [PMID: 29671152 DOI: 10.1007/s10439-018-2026-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/11/2018] [Indexed: 12/01/2022]
Abstract
We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot-ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3 cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.
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Affiliation(s)
- Yi-Chung Lin
- Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Jonathan P Walter
- CED Technologies, 6817 Southpoint Pkwy, Suite 1901, Jacksonville, FL, 32216, USA
| | - Marcus G Pandy
- Department of Mechanical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
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Banks CL, Pai MM, McGuirk TE, Fregly BJ, Patten C. Methodological Choices in Muscle Synergy Analysis Impact Differentiation of Physiological Characteristics Following Stroke. Front Comput Neurosci 2017; 11:78. [PMID: 28912707 PMCID: PMC5583217 DOI: 10.3389/fncom.2017.00078] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 08/02/2017] [Indexed: 11/18/2022] Open
Abstract
Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.
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Affiliation(s)
- Caitlin L Banks
- Neural Control of Movement Lab, Malcom Randall VA Medical CenterGainesville, FL, United States.,Department of Biomedical Engineering, University of FloridaGainesville, FL, United States.,Rehabilitation Science Doctoral Program, Department of Physical Therapy, University of FloridaGainesville, FL, United States
| | - Mihir M Pai
- Department of Mechanical and Aerospace Engineering, University of FloridaGainesville, FL, United States
| | - Theresa E McGuirk
- Neural Control of Movement Lab, Malcom Randall VA Medical CenterGainesville, FL, United States
| | - Benjamin J Fregly
- Department of Biomedical Engineering, University of FloridaGainesville, FL, United States.,Department of Mechanical and Aerospace Engineering, University of FloridaGainesville, FL, United States
| | - Carolynn Patten
- Neural Control of Movement Lab, Malcom Randall VA Medical CenterGainesville, FL, United States.,Rehabilitation Science Doctoral Program, Department of Physical Therapy, University of FloridaGainesville, FL, United States
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Meyer AJ, Patten C, Fregly BJ. Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry. PLoS One 2017; 12:e0179698. [PMID: 28700708 PMCID: PMC5507406 DOI: 10.1371/journal.pone.0179698] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 06/02/2017] [Indexed: 12/13/2022] Open
Abstract
Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient’s lower extremity muscle excitations contribute to the patient’s lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient’s musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.
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Affiliation(s)
- Andrew J. Meyer
- Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, United States of America
| | - Carolynn Patten
- Department of Physical Therapy, University of Florida, Gainesville, FL, United States of America
- Neural Control of Movement Lab, Malcom Randall VA Medical Center, Gainesville, FL, United States of America
| | - Benjamin J. Fregly
- Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL, United States of America
- * E-mail:
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Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis. ENTROPY 2017. [DOI: 10.3390/e19050229] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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