1
|
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.
Collapse
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
| |
Collapse
|
2
|
Michaud F, Mouzo F, Dopico D, Cuadrado J. A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation. SENSORS (BASEL, SWITZERLAND) 2024; 24:3042. [PMID: 38793897 PMCID: PMC11125272 DOI: 10.3390/s24103042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model's predictive capabilities and its applicability to practical scenarios. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench to replicate a knee subject to a total knee replacement. The original knee inserts of the training station were replaced with personalized 3D-printed bones incorporating their corresponding implants, and multiple sensors with their respective supports were added. The recorded movement of the patella was used in combination with the forces recorded by the pressure sensor and the load cells, to validate the results obtained from the simulation, which was performed by means of a multibody dynamics formulation implemented in a custom-developed library. The utilization of 3D-printed models and sensors facilitated cost-effective and replicable experimental validation of computational simulations, thereby advancing orthopedic surgery while circumventing ethical concerns.
Collapse
Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, Centro de Investigación en Tecnologías Navales e Industriales (CITENI), Campus Industrial de Ferrol, University of La Coruña, 15403 Ferrol, Spain; (F.M.); (D.D.); (J.C.)
| | | | | | | |
Collapse
|
3
|
Ueno R, Tsuyuki Y, Tohyama H. Validity of muscle activation estimated with predicted ground reaction force in inverse dynamics based musculoskeletal simulation during gait. J Biomech 2024; 168:112118. [PMID: 38677028 DOI: 10.1016/j.jbiomech.2024.112118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/06/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
The inverse dynamics based musculoskeletal simulation needs ground reaction forces (GRF) as an external force input. GRF can be predicted from kinematic data. However, the validity of estimated muscle activation using the predicted GRF has remained unclear. Therefore, the purpose of this study was to determine the validity of estimated muscle activation with predicted GRF in the inverse dynamics based musculoskeletal simulation. To perform musculoskeletal simulations, an open-source motion capture dataset that contains gait data from 50 healthy subjects was used. CusToM was used for the musculoskeletal simulations. Two sets of inverse dynamics and static optimization were performed, one used predicted GRF (PRED) and another used experimentally measured GRF (EXP). Pearson's correlation was calculated to evaluate the similarity between EMG and estimated muscle activations for both PRED and EXP. To compare PRED and EXP, paired t-tests were used to compare the trial-wise muscle activation similarity and residuals. Relationships between joint moments and residuals were also tested. The overall muscle activation similarity was comparable in PRED (R = 0.477) and EXP (R = 0.475). The residuals were 2-4 times higher in EXP compared to PRED (P < 0.001). The hip flexion-extension moment was correlated to sagittal plane residual moment (R = 0.467). The muscle activations estimated using predicted GRF were comparable to that with measured GRF in the inverse dynamics based musculoskeletal simulation. Prediction of GRF helps to perform musculoskeletal simulations where the force plates are not available.
Collapse
Affiliation(s)
- Ryo Ueno
- Department of Research and Development, ORGO, Sapporo, Japan; Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
| | - Yasuaki Tsuyuki
- Department of Research and Development, ORGO, Sapporo, Japan
| | | |
Collapse
|
4
|
Kaya Keles CS, Ates F. How mechanics of individual muscle-tendon units define knee and ankle joint function in health and cerebral palsy-a narrative review. Front Bioeng Biotechnol 2023; 11:1287385. [PMID: 38116195 PMCID: PMC10728775 DOI: 10.3389/fbioe.2023.1287385] [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: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023] Open
Abstract
This study reviews the relationship between muscle-tendon biomechanics and joint function, with a particular focus on how cerebral palsy (CP) affects this relationship. In healthy individuals, muscle size is a critical determinant of strength, with muscle volume, cross-sectional area, and moment arm correlating with knee and ankle joint torque for different isometric/isokinetic contractions. However, in CP, impaired muscle growth contributes to joint pathophysiology even though only a limited number of studies have investigated the impact of deficits in muscle size on pathological joint function. As muscles are the primary factors determining joint torque, in this review two main approaches used for muscle force quantification are discussed. The direct quantification of individual muscle forces from their relevant tendons through intraoperative approaches holds a high potential for characterizing healthy and diseased muscles but poses challenges due to the invasive nature of the technique. On the other hand, musculoskeletal models, using an inverse dynamic approach, can predict muscle forces, but rely on several assumptions and have inherent limitations. Neither technique has become established in routine clinical practice. Nevertheless, identifying the relative contribution of each muscle to the overall joint moment would be key for diagnosis and formulating efficient treatment strategies for patients with CP. This review emphasizes the necessity of implementing the intraoperative approach into general surgical practice, particularly for joint correction operations in diverse patient groups. Obtaining in vivo data directly would enhance musculoskeletal models, providing more accurate force estimations. This integrated approach can improve the clinicians' decision-making process and advance treatment strategies by predicting changes at the muscle and joint levels before interventions, thus, holding the potential to significantly enhance clinical outcomes.
Collapse
|
5
|
Lloyd DG, Jonkers I, Delp SL, Modenese L. The History and Future of Neuromusculoskeletal Biomechanics. J Appl Biomech 2023; 39:273-283. [PMID: 37751904 DOI: 10.1123/jab.2023-0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 09/28/2023]
Abstract
The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.
Collapse
Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, School of Health Science and Social Work, Griffith University, Gold Coast, QLD, Australia
| | - Ilse Jonkers
- Institute of Physics-Based Modeling for in Silico Health, Human Movement Science Department, KU Leuven, Leuven, Belgium
| | - Scott L Delp
- Bioengineering, Mechanical Engineering and Orthopedic Surgery, and Wu Tsai Human Performance Alliance at Stanford, Stanford University, Stanford, CA, USA
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Ebers MR, Rosenberg MC, Kutz JN, Steele KM. A machine learning approach to quantify individual gait responses to ankle exoskeletons. J Biomech 2023; 157:111695. [PMID: 37406604 DOI: 10.1016/j.jbiomech.2023.111695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023]
Abstract
Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.
Collapse
Affiliation(s)
- Megan R Ebers
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
| | - Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
8
|
Uhlrich SD, Uchida TK, Lee MR, Delp SL. Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future. J Biomech 2023; 154:111623. [PMID: 37210923 PMCID: PMC10544733 DOI: 10.1016/j.jbiomech.2023.111623] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/05/2023] [Indexed: 05/23/2023]
Abstract
Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.
Collapse
Affiliation(s)
- Scott D Uhlrich
- Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
| | - Thomas K Uchida
- Department of Mechanical Engineering, University of Ottawa, 161 Louis-Pasteur, Ottawa, ON K1N 6N5, Canada.
| | - Marissa R Lee
- Department of Mechanical Engineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
| | - Scott L Delp
- Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA; Department of Mechanical Engineering, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA; Department of Orthopaedic Surgery, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
| |
Collapse
|
9
|
Ebers MR, Rosenberg MC, Kutz JN, Steele KM. A machine learning approach to quantify individual gait responses to ankle exoskeletons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524757. [PMID: 36711530 PMCID: PMC9882260 DOI: 10.1101/2023.01.20.524757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton ( Exo ) gait, and (iii) the Discrepancy ( i.e. , response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R 2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), ( p < 0.042)) than the Nominal model (median R 2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R 2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.
Collapse
Affiliation(s)
- Megan R Ebers
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
10
|
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.
Collapse
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,
| |
Collapse
|
11
|
Davico G, Lloyd DG, Carty CP, Killen BA, Devaprakash D, Pizzolato C. Multi-level personalization of neuromusculoskeletal models to estimate physiologically plausible knee joint contact forces in children. Biomech Model Mechanobiol 2022; 21:1873-1886. [PMID: 36229699 DOI: 10.1007/s10237-022-01626-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/05/2022] [Indexed: 11/02/2022]
Abstract
Neuromusculoskeletal models are a powerful tool to investigate the internal biomechanics of an individual. However, commonly used neuromusculoskeletal models are generated via linear scaling of generic templates derived from elderly adult anatomies and poorly represent a child, let alone children with a neuromuscular disorder whose musculoskeletal structures and muscle activation patterns are profoundly altered. Model personalization can capture abnormalities and appropriately describe the underlying (altered) biomechanics of an individual. In this work, we explored the effect of six different levels of neuromusculoskeletal model personalization on estimates of muscle forces and knee joint contact forces to tease out the importance of model personalization for normal and abnormal musculoskeletal structures and muscle activation patterns. For six children, with and without cerebral palsy, generic scaled models were developed and progressively personalized by (1) tuning and calibrating musculotendon units' parameters, (2) implementing an electromyogram-assisted approach to synthesize muscle activations, and (3) replacing generic anatomies with image-based bony geometries, and physiologically and physically plausible muscle kinematics. Biomechanical simulations of gait were performed in the OpenSim and CEINMS software on ten overground walking trials per participant. A mixed-ANOVA test, with Bonferroni corrections, was conducted to compare all models' estimates. The model with the highest level of personalization produced the most physiologically plausible estimates. Model personalization is crucial to produce physiologically plausible estimates of internal biomechanical quantities. In particular, personalization of musculoskeletal anatomy and muscle activation patterns had the largest effect overall. Increased research efforts are needed to ease the creation of personalized neuromusculoskeletal models.
Collapse
Affiliation(s)
- Giorgio Davico
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy. .,Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy. .,School of Allied Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - David G Lloyd
- School of Allied Health Sciences and Social Work, Griffith University, Gold Coast, Australia.,Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.,Department of Orthopaedics, Queensland Children's Hospital, Children's Health Queensland Hospital and Health Service, Brisbane, Australia
| | - Bryce A Killen
- School of Allied Health Sciences and Social Work, Griffith University, Gold Coast, Australia.,Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Daniel Devaprakash
- School of Allied Health Sciences and Social Work, Griffith University, Gold Coast, Australia.,Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia
| | - Claudio Pizzolato
- School of Allied Health Sciences and Social Work, Griffith University, Gold Coast, Australia.,Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Ziziene J, Daunoraviciene K, Juskeniene G, Raistenskis J. Comparison of kinematic parameters of children gait obtained by inverse and direct models. PLoS One 2022; 17:e0270423. [PMID: 35749351 PMCID: PMC9231751 DOI: 10.1371/journal.pone.0270423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 06/10/2022] [Indexed: 11/19/2022] Open
Abstract
The purpose of this study is to compare differences between kinematic parameters of pediatric gait obtained by direct kinematics (DK) (Plug-in-Gait) and inverse kinematics (IK) (AnyBody) models. Seventeen healthy children participated in this study. Both lower extremities were examined using a Vicon 8-camera motion capture system and a force plate. Angles of the hip, knee, and ankle joints were obtained based on DK and IK models, and ranges of motion (ROMs) were identified from them. The standard error of measurement, root-mean-squared error, correlation r, and magnitude-phase (MP) metrics were calculated to compare differences between the models’ outcomes. The determined standard error of measurement between ROMs from the DK and IK models ranged from 0.34° to 0.58°. A significant difference was found in the ROMs with the exception of the left hip’s internal/external rotation. The mean RMSE of all joints’ amplitudes exceeded the clinical significance limit and was 13.6 ± 4.0°. The best curve angles matching nature were found in the sagittal plane, where r was 0.79 to 0.83 and MP metrics were 0.05 to 0.30. The kinematic parameters of pediatric gait obtained by IK and DK differ significantly. Preferably, all of the results obtained by DK must be validated/verified by IK, in order to achieve a more accurate functional assessment of the individual. Furthermore, the use of IK expands the capabilities of gait analysis and allows for kinetic characterisation.
Collapse
Affiliation(s)
- Jurgita Ziziene
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Kristina Daunoraviciene
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Giedre Juskeniene
- Faculty of Medicine, Department of Rehabilitation, Physical and Sports Medicine, Health Science Institute, Vilnius University, Vilnius, Lithuania
| | - Juozas Raistenskis
- Faculty of Medicine, Department of Rehabilitation, Physical and Sports Medicine, Health Science Institute, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
14
|
Kapardi M, Pithapuram MV, Rangayyan YM, Iyengar RS, Singh AK, Sripada S, Raghavan M. In-silico neuro musculoskeletal model reproduces the movement types obtained by spinal micro stimulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106804. [PMID: 35436659 DOI: 10.1016/j.cmpb.2022.106804] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 03/25/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. Our major goal is to create a neuro- musculoskeletal upper limb in-silico model, which is modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics. METHODS The model is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. We further characterized and demonstrated the use of this model in generating a range of commonly observed upper limb movements by means of a spatio-temporal stimulation pattern delivered to the cervical spinal cord. RESULTS We were able to characterize the model based on proprioception (Ia, Ib and II fibers), afferent conduction delay and inital postures of the musculoskeletal system. A smooth movement was achieved in all the considered experiments. The generated movements in all degrees of freedom were reproduced in accordance with the previous experimental studies. CONCLUSION In this work, design and development of the upper limb model was described in a modular fashion, while reusing existing models and modules. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation.
Collapse
|
15
|
Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill. J Clin Med 2022; 11:jcm11040954. [PMID: 35207227 PMCID: PMC8880133 DOI: 10.3390/jcm11040954] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 12/10/2022] Open
Abstract
A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.
Collapse
|
16
|
A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11052037] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [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.
Collapse
|
17
|
Vandekerckhove I, Wesseling M, Kainz H, Desloovere K, Jonkers I. The effect of hip muscle weakness and femoral bony deformities on gait performance. Gait Posture 2021; 83:280-286. [PMID: 33227606 DOI: 10.1016/j.gaitpost.2020.10.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/31/2020] [Accepted: 10/20/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND Children with cerebral palsy (CP) present with a pathological gait pattern due to musculoskeletal impairments, such as muscle weakness and altered bony geometry. However, the effect of these impairments on gait performance is still unknown. Research aim:This study aimed to explore the effect of hip muscle weakness and femoral deformities on the gait performance of CP and typical developing (TD) subjects. METHODS 6400 musculoskeletal models were created by weakening the hip extensors, abductors, adductors and flexors from 0% to 75 % and increasing the femoral anteversion angle (FAA) and neck shaft angle (NSA) from 20° to 60° and 120° to 160°, respectively. One TD and five CP gait patterns were imposed to each model and muscle forces were calculated. The effect of weakness and bony deformities on the capability gap (CG) at the hip, i.e. the lack in hip moment generating capacity to perform the gait pattern, was investigated using regression analysis. RESULTS The CG of apparent equinus, stiff knee gait, TD gait, jump gait and true equinus increased with 0.080, 0.038, 0.015, 0.023 and 0.005 Nm/kg per 10 percent hip abductor weakness increase, with 0.211, 0.130, 0.056, 0.045 and 0.011 Nm/kg per 10 degrees FAA increase and with 0.163, 0.080, 0.036, 0.043 and 0.011 Nm/kg per 10 degrees NSA increase, respectively. Combined weakness and bony deformities explained 96 %, 85 %, 82 %, 65 %, 40 % and 35 % of the variance in the CG of apparent equinus, TD gait, stiff knee gait, jump gait, true equinus and crouch gait, respectively. SIGNIFICANCE The results suggest that surgical correction of femoral deformities is more likely to be effective than strength training of hip muscles in enhancing CP gait performance. Jump gait, true equinus and especially crouch were more robust, while apparent equinus and stiff knee gait were limited by hip weakness and femoral deformities.
Collapse
Affiliation(s)
- Ines Vandekerckhove
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium; Clinical Motion Analysis Laboratory, University Hospitals Leuven, Pellenberg, Belgium.
| | | | - Hans Kainz
- Department of Movement Sciences, KU Leuven, Leuven, Belgium; Centre for Sport Science and University Sports, Department of Biomechanics, Kinesiology and Computer Science in Sport, University of Vienna, Vienna, Austria
| | - Kaat Desloovere
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium; Clinical Motion Analysis Laboratory, University Hospitals Leuven, Pellenberg, Belgium
| | - Ilse Jonkers
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| |
Collapse
|
18
|
Automatic generation of personalised skeletal models of the lower limb from three-dimensional bone geometries. J Biomech 2020; 116:110186. [PMID: 33515872 DOI: 10.1016/j.jbiomech.2020.110186] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/06/2020] [Accepted: 12/11/2020] [Indexed: 02/07/2023]
Abstract
The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1° (knee joint) to 11° (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.
Collapse
|
19
|
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] [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.
Collapse
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
| |
Collapse
|
20
|
In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice? APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10207255] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.
Collapse
|
21
|
Rosenberg MC, Banjanin BS, Burden SA, Steele KM. Predicting walking response to ankle exoskeletons using data-driven models. J R Soc Interface 2020; 17:20200487. [PMID: 33050782 DOI: 10.1098/rsif.2020.0487] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.
Collapse
Affiliation(s)
- Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Bora S Banjanin
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Samuel A Burden
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| |
Collapse
|
22
|
Campbell R, Tipping N, Carty C, Walsh J, Johnson L. Orthopaedic management of knee joint impairment in cerebral palsy: A systematic review and meta-analysis. Gait Posture 2020; 80:347-360. [PMID: 32615408 DOI: 10.1016/j.gaitpost.2020.06.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 06/01/2020] [Accepted: 06/14/2020] [Indexed: 02/02/2023]
Abstract
BACKGROUND The optimal management of impaired knee joint function in patients with cerebral palsy (CP) remains a significant and ongoing challenge in paediatric orthopaedic surgery. RESEARCH QUESTION What are the clinical and functional outcomes after operative and non-operative orthopaedic interventions for knee joint impairment in patients with CP? METHODS This systematic review and meta-analysis of orthopaedic interventions for the management of knee joint impairment in paediatric CP patients evaluated study-level data in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. We performed searches of the following electronic databases from their dates of inception to November 2019: Medline (Ovid), Embase (Ovid) and Pubmed. We extracted mean differences in pre-operative and post-operative measurements for the following outcomes: minimum knee flexion in stance; knee flexion at initial contact; maximum knee flexion in swing; range of motion; popliteal angle; fixed flexion deformity angle; and mean pelvic tilt. RESULTS Sixty-nine retrospective cohort studies, prospective cohort studies and RCTs comprising 2991 patients were included with 4578 knees analysed. Included studies were of sufficient quality as assessed by the MOOSE checklist. Operative interventions showed significant improvement in knee flexion at initial contact, knee flexion in stance, range of motion, popliteal angle and fixed flexion deformity which were comparable when subgrouped according to operative technique. In contrast, non-operative techniques and botulinum toxin injection did not confer significant improvements. Operative interventions for knee joint impairment led to increased mean pelvic tilt and reduced maximum knee flexion in swing. SIGNIFICANCE This review provides strong evidence that operative interventions for the management of knee joint impairment in cerebral palsy patients improve knee kinematics and clinical examination findings.
Collapse
Affiliation(s)
- Ryan Campbell
- Department of Medicine, University of New South Wales, Australia.
| | - Nicholas Tipping
- Department of Medicine, University of New South Wales, Australia
| | - Christopher Carty
- School of Allied Health Sciences and GCORE, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia; Department of Orthopaedics, Queensland Children's Hospital, QLD 4101, Australia
| | - John Walsh
- Department of Orthopaedics, Queensland Children's Hospital, QLD 4101, Australia
| | - Liam Johnson
- Department of Orthopaedics, Queensland Children's Hospital, QLD 4101, Australia
| |
Collapse
|
23
|
Falisse A, Pitto L, Kainz H, Hoang H, Wesseling M, Van Rossom S, Papageorgiou E, Bar-On L, Hallemans A, Desloovere K, Molenaers G, Van Campenhout A, De Groote F, Jonkers I. Physics-Based Simulations to Predict the Differential Effects of Motor Control and Musculoskeletal Deficits on Gait Dysfunction in Cerebral Palsy: A Retrospective Case Study. Front Hum Neurosci 2020; 14:40. [PMID: 32132911 PMCID: PMC7040166 DOI: 10.3389/fnhum.2020.00040] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 01/27/2020] [Indexed: 12/05/2022] Open
Abstract
Physics-based simulations of walking have the theoretical potential to support clinical decision-making by predicting the functional outcome of treatments in terms of walking performance. Yet before using such simulations in clinical practice, their ability to identify the main treatment targets in specific patients needs to be demonstrated. In this study, we generated predictive simulations of walking with a medical imaging based neuro-musculoskeletal model of a child with cerebral palsy presenting crouch gait. We explored the influence of altered muscle-tendon properties, reduced neuromuscular control complexity, and spasticity on gait dysfunction in terms of joint kinematics, kinetics, muscle activity, and metabolic cost of transport. We modeled altered muscle-tendon properties by personalizing Hill-type muscle-tendon parameters based on data collected during functional movements, simpler neuromuscular control by reducing the number of independent muscle synergies, and spasticity through delayed muscle activity feedback from muscle force and force rate. Our simulations revealed that, in the presence of aberrant musculoskeletal geometries, altered muscle-tendon properties rather than reduced neuromuscular control complexity and spasticity were the primary cause of the crouch gait pattern observed for this child, which is in agreement with the clinical examination. These results suggest that muscle-tendon properties should be the primary target of interventions aiming to restore an upright gait pattern for this child. This suggestion is in line with the gait analysis following muscle-tendon property and bone deformity corrections. Future work should extend this single case analysis to more patients in order to validate the ability of our physics-based simulations to capture the gait patterns of individual patients pre- and post-treatment. Such validation would open the door for identifying targeted treatment strategies with the aim of designing optimized interventions for neuro-musculoskeletal disorders.
Collapse
Affiliation(s)
| | - Lorenzo Pitto
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Hans Kainz
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Hoa Hoang
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | | | - Sam Van Rossom
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | | | - Lynn Bar-On
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium.,Department of Rehabilitation Medicine, Amsterdam Movement Sciences, Amsterdam UMC, VU University Medical Center, Amsterdam, Netherlands
| | - Ann Hallemans
- Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium
| | - Kaat Desloovere
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Guy Molenaers
- Department of Orthopaedic Surgery, UZ Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Anja Van Campenhout
- Department of Orthopaedic Surgery, UZ Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | | | - Ilse Jonkers
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| |
Collapse
|