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Xia Z, Cornish BM, Devaprakash D, Barrett RS, Lloyd DG, Hams AH, Pizzolato C. Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2070-2077. [PMID: 38787676 DOI: 10.1109/tnsre.2024.3403092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) ≤ 0.21 , R 2 ≥ 0.81 . Walking task resulted the most accurate with RMSE = 189±62 N; nRMSE = 0.11±0.03 , R 2 = 0.92±0.04 . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.
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Zhang L, Van Wouwe T, Yan S, Wang R. EMG-Constrained and Ultrasound-Informed Muscle-Tendon Parameter Estimation in Post-Stroke Hemiparesis. IEEE Trans Biomed Eng 2024; 71:1798-1809. [PMID: 38206783 DOI: 10.1109/tbme.2024.3352556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Secondary morphological and mechanical property changes in the muscle-tendon unit at the ankle joint are often observed in post-stroke individuals. These changes may alter the force generation capacity and affect daily activities such as locomotion. This work aimed to estimate subject-specific muscle-tendon parameters in individuals after stroke by solving the muscle redundancy problem using direct collocation optimal control methods based on experimental electromyography (EMG) signals and measured muscle fiber length. Subject-specific muscle-tendon parameters of the gastrocnemius, soleus, and tibialis anterior were estimated in seven post-stroke individuals and seven healthy controls. We found that the maximum isometric force, tendon stiffness and optimal fiber length in the post-stroke group were considerably lower than in the control group. We also computed the root mean square error between estimated and experimental values of muscle excitation and fiber length. The musculoskeletal model with estimated subject-specific muscle tendon parameters (from the muscle redundancy solver), yielded better muscle excitation and fiber length estimations than did scaled generic parameters. Our findings also showed that the muscle redundancy solver can estimate muscle-tendon parameters that produce force behavior in better accordance with the experimentally-measured value. These muscle-tendon parameters in the post-stroke individuals were physiologically meaningful and may shed light on treatment and/or rehabilitation planning.
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Sivakumar A, Bennett KJ, Pizzolato C, Rickman M, Thewlis D. Hip biomechanics in early recovery following fixation of intertrochanteric fractures: Results from a randomised controlled trial. J Biomech 2024; 170:112169. [PMID: 38795542 DOI: 10.1016/j.jbiomech.2024.112169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/25/2024] [Accepted: 05/21/2024] [Indexed: 05/28/2024]
Abstract
Single and dual integrated screw femoral nails are both commonly used to treat intertrochanteric fractures. This study investigated if using single or dual integrated screw femoral nails result in different post-operative hip joint loading. In the presence of differences, we investigated potential contributing factors. Patients were randomised for treatment via single screw (Stryker, Gamma3) or dual-integrated screw nail (Smith and Nephew, Intertan). Pre-injury mobility levels were collected at enrolment. Hip radiographs and gait data were collected at six weeks (Gamma: 16; Intertan: 15) and six months (Gamma: 14; Intertan: 13) follow-up. The resultant hip joint reaction forces and abductor muscle forces were estimated using electromyography-assisted neuromusculoskeletal modelling during level walking gait. Our primary analysis focused on the resultant hip joint reaction force and abductor muscle forces. We compared between groups, across stance phase of walking gait, using statistical parametric mapping. At six weeks, the Intertan group showed a short (∼5% of stance phase) but substantial (33 % [0.3 × body weight] greater magnitude) resultant hip joint reaction force when compared to the Gamma group (P = 0.022). Higher gluteus medius forces (P = 0.009) were demonstrated in the Intertan group at six weeks. Harris Hip Scores followed the trend seen for the biomechanical outcomes with superior scores for the Intertan group at six weeks postoperative (P = 0.044). The use of dual-integrated screw femoral nails over single screw devices may allow for hip biomechanics more closely resembling normal hip function at earlier post-operative timepoints, but these appear to resolve by six months postoperative.
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Affiliation(s)
- Arjun Sivakumar
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, South Australia, Australia.
| | - Kieran J Bennett
- The Medical Device Research Institute, Flinders University, South Australia, Australia.
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering, Griffith University, Queensland, Australia.
| | - Mark Rickman
- Department of Orthopaedics & Trauma, Royal Adelaide Hospital, South Australia, Australia.
| | - Dominic Thewlis
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, South Australia, Australia.
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Cornish BM, Pizzolato C, Saxby DJ, Xia Z, Devaprakash D, Diamond LE. Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis. Osteoarthritis Cartilage 2024; 32:730-739. [PMID: 38442767 DOI: 10.1016/j.joca.2024.02.891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/23/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
OBJECTIVE To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. To assess the capability of the neural network to detect directional changes in HCF resulting from prescribed gait modifications. DESIGN A calibrated electromyography-informed neuromusculoskeletal model was used to compute lower body joint angles, moments, and HCF for 17 participants with mild-to-moderate hip OA. Anatomical key points (e.g., joint centres) were synthesised from marker trajectories and augmented with bias and noise expected from computer vision-based pose estimation systems. Temporal convolutional and long short-term memory neural networks (NN) were trained using leave-one-subject-out validation to predict neuromusculoskeletal modelling outputs from the synthesised key points and measured electromyography data from 5 hip-spanning muscles. RESULTS HCF was predicted with an average error of 13.4 ± 7.1% of peak force. Joint angles and moments were predicted with an average root-mean-square-error of 5.3 degrees and 0.10 Nm/kg, respectively. The NN could detect changes in peak HCF that occur due to gait modifications with good agreement with neuromusculoskeletal modelling (r2 = 0.72) and a minimum detectable change of 9.5%. CONCLUSION The developed neural network predicted HCF and lower body joint angles and moments in individuals with hip OA using noisy synthesised key point locations with acceptable errors. Changes in HCF magnitude due to gait modifications were predicted with high accuracy. These findings have important implications for implementation of load-modification based gait retraining interventions for people with hip OA in a natural environment (i.e., home, clinic).
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Affiliation(s)
- Bradley M Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia; Vald Performance, Brisbane, Australia.
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
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Caillet AH, Phillips ATM, Modenese L, Farina D. NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo. J Electromyogr Kinesiol 2024; 76:102873. [PMID: 38518426 DOI: 10.1016/j.jelekin.2024.102873] [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] [Indexed: 03/24/2024] Open
Abstract
The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.
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Affiliation(s)
| | - Andrew T M Phillips
- Department of Civil and Environmental Engineering, Imperial College London, UK
| | - Luca Modenese
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.
| | - Dario Farina
- Department of Bioengineering, Imperial College London, UK.
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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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Bavil AY, Eghan-Acquah E, Diamond LE, Barrett R, Carty CP, Barzan M, Nasseri A, Lloyd DG, Saxby DJ, Feih S. Effect of different constraining boundary conditions on simulated femoral stresses and strains during gait. Sci Rep 2024; 14:10808. [PMID: 38734763 PMCID: PMC11088641 DOI: 10.1038/s41598-024-61305-x] [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: 10/22/2023] [Accepted: 05/03/2024] [Indexed: 05/13/2024] Open
Abstract
Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for both healthy and pathological femurs.
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Affiliation(s)
- Alireza Y Bavil
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Emmanuel Eghan-Acquah
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Rod Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia.
- School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia.
| | - Stefanie Feih
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Gold Coast, Australia.
- Advanced Design and Prototyping Technologies (ADaPT) Institute, Griffith University, Gold Coast, Australia.
- School of Engineering and Built Environment, Griffith University, Gold Coast, Australia.
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Dastgerdi AK, Esrafilian A, Carty CP, Nasseri A, Barzan M, Korhonen RK, Astori I, Hall W, Saxby DJ. Sensitivity analysis of paediatric knee kinematics to the graft surgical parameters during anterior cruciate ligament reconstruction: A sequentially linked neuromusculoskeletal-finite element analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108132. [PMID: 38503071 DOI: 10.1016/j.cmpb.2024.108132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/26/2024] [Accepted: 03/08/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Incidence of paediatric anterior cruciate ligament (ACL) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence guiding these decisions. This study presents a systemmatic computational approach to study effects of surgical parameter variation on kinematics of paediatric knees. METHODS This study used sequentially-linked neuromusculoskeletal (NMSK) finite element (FE) models of three paediatric knees to estimate the: (i) sensitivity of post-operative knee kinematics to four surgical parameters (type, size, insertion, and pre-tension) through multi-input multi-output sensitivity analysis; (ii) influence of motion and loading conditions throughout stance phase of walking gait on sensitivity indices; and (iii) influence of subject-specific anatomy (i.e., knee size) on sensitivivty indices. A previously validated FE model of the intact knee for each subject served as a reference against which ACLR knee kinematics were compared. RESULTS Sensitivity analyses revealed significant influences of surgical parameters on ACLR knee kinematics, albeit without discernible trend favouring any one parameter. Graft size and pre-tension were primary drivers of variation in knee translations and rotations, however, their effects fluctuated across stance indicating motion and loading conditions affect system sensitivity to surgical parameters. Importantly, the sensitivity of knee kinematics to surgical parameter varied across subjects, indicating geometry (i.e., knee size) influenced system sensitivity. Notably, alterations in graft parameters yielded substantial effects on kinematics (normalized root-mean-square-error > 10 %) compared to intact knee models, indicating surgical parameters vary post-operative knee kinematics. CONCLUSIONS Overall, this initial study highlights the importance of surgical parameter selection on post-operative kinematics in the paediatric ACLR knee, and provides evidence of the need for personalized surgical planning to ultimately enhance patient outcomes.
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Affiliation(s)
- Ayda Karimi Dastgerdi
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia.
| | - Amir Esrafilian
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia; Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Ivan Astori
- Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Wayne Hall
- School of Engineering and Built Environment, Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia
| | - David John Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
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Assila N, Begon M, Duprey S. Finite Element Model of the Shoulder with Active Rotator Cuff Muscles: Application to Wheelchair Propulsion. Ann Biomed Eng 2024; 52:1240-1254. [PMID: 38376768 DOI: 10.1007/s10439-024-03449-5] [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: 09/06/2023] [Accepted: 01/09/2024] [Indexed: 02/21/2024]
Abstract
The rotator cuff is prone to injury, remarkably so for manual wheelchair users. To understand its pathomechanisms, finite element models incorporating three-dimensional activated muscles are needed to predict soft tissue strains during given tasks. This study aimed to develop such a model to understand pathomechanisms associated with wheelchair propulsion. We developed an active muscle model associating a passive fiber-reinforced isotropic matrix with an activation law linking calcium ion concentration to tissue tension. This model was first evaluated against known physiological muscle behavior; then used to activate the rotator cuff during a wheelchair propulsion cycle. Here, experimental kinematics and electromyography data was used to drive a shoulder finite element model. Finally, we evaluated the importance of muscle activation by comparing the results of activated and non-activated rotator cuff muscles during both propulsion and isometric contractions. Qualitatively, the muscle constitutive law reasonably reproduced the classical Hill model force-length curve and the behavior of a transversally loaded muscle. During wheelchair propulsion, the deformation and fiber stretch of the supraspinatus muscle-tendon unit pointed towards the possibility for this tendon to develop tendinosis due to the multiaxial loading imposed by the kinematics of propulsion. Finally, differences in local stretch and positions of the lines of action between activated and non-activated models were only observed at activation levels higher than 30%. Our novel finite element model with active muscles is a promising tool for understanding the pathomechanisms of the rotator cuff for various dynamic tasks, especially those with high muscle activation levels.
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Affiliation(s)
- Najoua Assila
- School of Kinesiology and Exercise Sciences, Faculty of Medicine, University of Montréal, Montréal, QC, Canada.
- Research Center of the Sainte-Justine University Hospital Center, Montréal, QC, Canada.
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T_9406, 69622, Lyon, France.
| | - Mickaël Begon
- School of Kinesiology and Exercise Sciences, Faculty of Medicine, University of Montréal, Montréal, QC, Canada
- Research Center of the Sainte-Justine University Hospital Center, Montréal, QC, Canada
| | - Sonia Duprey
- Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR T_9406, 69622, Lyon, France
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Sturdy JT, Sessoms PH, Silverman AK. Psoas force recruitment in full-body musculoskeletal movement simulations is restored with a geometrically informed cost function weighting. J Biomech 2024; 168:112130. [PMID: 38713998 DOI: 10.1016/j.jbiomech.2024.112130] [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: 09/29/2023] [Revised: 04/01/2024] [Accepted: 04/29/2024] [Indexed: 05/09/2024]
Abstract
Simulations of musculoskeletal models are useful for estimating internal muscle and joint forces. However, predicted forces rely on optimization and modeling formulations. Geometric detail is important to predict muscle forces, and greater geometric complexity is required for muscles that have broad attachments or span many joints, as in the torso. However, the extent to which optimized muscle force recruitment is sensitive to these geometry choices is unclear. We developed level, uphill and downhill sloped walking simulations using a standard (uniformly weighted, "fatigue-like") cost function with lower limb and full-body musculoskeletal models to evaluate hip muscle recruitment with different geometric representations of the psoas muscle under walking conditions with varying hip moment demands. We also tested a novel cost function formulation where muscle activations were weighted according to the modeled geometric detail in the full-body model. Total psoas force was less and iliacus, rectus femoris, and other hip flexors' force was greater when psoas was modeled with greater geometric detail compared to other hip muscles for all slopes. The proposed weighting scheme restored hip muscle force recruitment without sacrificing detailed psoas geometry. In addition, we found that lumbar, but not hip, joint contact forces were influenced by psoas force recruitment. Our results demonstrate that static optimization dependent simulations using models comprised of muscles with different amounts of geometric detail bias force recruitment toward muscles with less geometric detail. Muscle activation weighting that accounts for differences in geometric complexity across muscles corrects for this recruitment bias.
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Affiliation(s)
- Jordan T Sturdy
- Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, USA.
| | - Pinata H Sessoms
- Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
| | - Anne K Silverman
- Department of Mechanical Engineering, Colorado School of Mines, Golden, CO, USA; Quantitative Biosciences and Engineering, Colorado School of Mines, Golden, CO, USA
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Cowburn J, Serrancolí G, Colyer S, Cazzola D. Optimal fibre length and maximum isometric force are the most influential parameters when modelling muscular adaptations to unloading using Hill-type muscle models. Front Physiol 2024; 15:1347089. [PMID: 38694205 PMCID: PMC11061504 DOI: 10.3389/fphys.2024.1347089] [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: 11/30/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction: Spaceflight is associated with severe muscular adaptations with substantial inter-individual variability. A Hill-type muscle model is a common method to replicate muscle physiology in musculoskeletal simulations, but little is known about how the underlying parameters should be adjusted to model adaptations to unloading. The aim of this study was to determine how Hill-type muscle model parameters should be adjusted to model disuse muscular adaptations. Methods: Isokinetic dynamometer data were taken from a bed rest campaign and used to perform tracking simulations at two knee extension angular velocities (30°·s-1 and 180°·s-1). The activation and contraction dynamics were solved using an optimal control approach and direct collocation method. A Monte Carlo sampling technique was used to perturb muscle model parameters within physiological boundaries to create a range of theoretical and feasible parameters to model muscle adaptations. Results: Optimal fibre length could not be shortened by more than 67% and 61% for the knee flexors and non-knee muscles, respectively. Discussion: The Hill-type muscle model successfully replicated muscular adaptations due to unloading, and recreated salient features of muscle behaviour associated with spaceflight, such as altered force-length behaviour. Future researchers should carefully adjust the optimal fibre lengths of their muscle-models when trying to model adaptations to unloading, particularly muscles that primarily operate on the ascending and descending limbs of the force-length relationship.
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Affiliation(s)
- James Cowburn
- Department for Health, University of Bath, Bath, United Kingdom
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Gil Serrancolí
- Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Steffi Colyer
- Department for Health, University of Bath, Bath, United Kingdom
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Dario Cazzola
- Department for Health, University of Bath, Bath, United Kingdom
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
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12
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Crossley CB, Diamond LE, Saxby DJ, de Sousa A, Lloyd DG, Che Fornusek, Pizzolato C. Joint contact forces during semi-recumbent seated cycling. J Biomech 2024; 168:112094. [PMID: 38640830 DOI: 10.1016/j.jbiomech.2024.112094] [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: 10/06/2023] [Revised: 03/07/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
Semi-recumbent cycling performed from a wheelchair is a popular rehabilitation exercise following spinal cord injury (SCI) and is often paired with functional electrical stimulation. However, biomechanical assessment of this cycling modality is lacking, even in unimpaired populations, hindering the development of personalised and safe rehabilitation programs for those with SCI. This study developed a computational pipeline to determine lower limb kinematics, kinetics, and joint contact forces (JCF) in 11 unimpaired participants during voluntary semi-recumbent cycling using a rehabilitation ergometer. Two cadences (40 and 60 revolutions per minute) and three crank powers (15 W, 30 W, and 45 W) were assessed. A rigid body model of a rehabilitation ergometer was combined with a calibrated electromyogram-informed neuromusculoskeletal model to determine JCF at the hip, knee, and ankle. Joint excursions remained consistent across all cadence and powers, but joint moments and JCF differed between 40 and 60 revolutions per minute, with peak JCF force significantly greater at 40 compared to 60 revolutions per minute for all crank powers. Poor correlations were found between mean crank power and peak JCF across all joints. This study provides foundation data and computational methods to enable further evaluation and optimisation of semi-recumbent cycling for application in rehabilitation after SCI and other neurological disorders.
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Affiliation(s)
- Claire B Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; Research Centre for Biomedical Engineering (CREB) at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Che Fornusek
- Exercise & Sports Science, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Griffith University, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
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13
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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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14
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Gonçalves BAM, Saxby DJ, Meinders E, Barrett RS, Diamond LE. Hip Contact Forces During Sprinting in Femoroacetabular Impingement Syndrome. Med Sci Sports Exerc 2024; 56:402-410. [PMID: 37882088 DOI: 10.1249/mss.0000000000003320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
PURPOSE Sprinting often provokes hip pain in individuals with femoroacetabular impingement syndrome (FAIS). Asphericity of the femoral head-neck junction (cam morphology) characteristic of FAIS can increase the risk of anterior-superior acetabular cartilage damage. This study aimed to 1) compare hip contact forces (magnitude and direction) during sprinting between individuals with FAIS, asymptomatic cam morphology (CAM), and controls without cam morphology, and 2) identify the phases of sprinting with high levels of anteriorly directed hip contact forces. METHODS Forty-six recreationally active individuals with comparable levels of physical activity were divided into three groups (FAIS, 14; CAM, 15; control, 17) based on their history of hip/groin pain, results of clinical impingement tests, and presence of cam morphology (alpha angle >55°). Three-dimensional marker trajectories, ground reaction forces, and electromyograms from 12 lower-limb muscles were recorded during 10-m overground sprinting trials. A linearly scaled electromyogram-informed neuromusculoskeletal model was used to calculate hip contact force magnitude (resultant, anterior-posterior, inferior-superior, medio-lateral) and angle (sagittal and frontal planes). Between-group comparisons were made using two-sample t -tests via statistical parametric mapping ( P < 0.05). RESULTS No significant differences in magnitude or direction of hip contact forces were observed between FAIS and CAM or between FAIS and control groups during any phase of the sprint cycle. The highest anteriorly directed hip contact forces were observed during the initial swing phase of the sprint cycle. CONCLUSIONS Hip contact forces during sprinting do not differentiate recreationally active individuals with FAIS from asymptomatic individuals with and without cam morphology. Hip loading during early swing, where peak anterior loading occurs, may be a potential mechanism for cartilage damage during sprinting-related sports in individuals with FAIS and/or asymptomatic cam morphology.
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15
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Kainz H, Koller W, Wallnöfer E, Bader TR, Mindler GT, Kranzl A. A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining. Sci Rep 2024; 14:3567. [PMID: 38347085 PMCID: PMC10861532 DOI: 10.1038/s41598-024-53857-9] [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: 06/12/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°-29°, tibial torsion 19°-38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°-52°, tibial torsion 3°-50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.
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Affiliation(s)
- 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 II), 1150, Vienna, Austria.
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, 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 II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, 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 II), 1150, Vienna, Austria
- Neuromechanics Research Group, Centre for Sport Science and University Sports, University of Vienna, Vienna, Austria
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Vienna, Austria
| | - Till R Bader
- Department of Radiology, Orthopaedic Hospital Speising, Vienna, Austria
| | - Gabriel T Mindler
- Department of Paediatric Orthopaedics and Foot Surgery, Orthopaedic Hospital Speising, Vienna, Austria
- Vienna Bone and Growth Center, Vienna, Austria
| | - Andreas Kranzl
- Vienna Bone and Growth Center, Vienna, Austria
- Laboratory for Gait and Movement Analysis, Orthopaedic Hospital Speising, Vienna, Austria
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16
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Chen Y, Yu W, Benali A, Lu D, Kok SY, Wang R. Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton. Front Aging Neurosci 2024; 16:1327397. [PMID: 38371400 PMCID: PMC10870425 DOI: 10.3389/fnagi.2024.1327397] [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: 10/24/2023] [Accepted: 01/05/2024] [Indexed: 02/20/2024] Open
Abstract
The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.
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Affiliation(s)
- Yasheng Chen
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Yu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Abderraouf Benali
- LISV, Versailles Systems Engineering Laboratory, Université de Versailles Saint Quentin en Yvelines, Paris, France
| | - Donglai Lu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Siong Yuen Kok
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Runxiao Wang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China
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17
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Diamond LE, Grant T, Uhlrich SD. Osteoarthritis year in review 2023: Biomechanics. Osteoarthritis Cartilage 2024; 32:138-147. [PMID: 38043858 DOI: 10.1016/j.joca.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modeling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modeling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.
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Affiliation(s)
- Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Tamara Grant
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
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18
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Mohamed Refai MI, Moya-Esteban A, Sartori M. Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting. J Biomech 2024; 164:111987. [PMID: 38342053 DOI: 10.1016/j.jbiomech.2024.111987] [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: 05/26/2023] [Revised: 01/29/2024] [Accepted: 02/04/2024] [Indexed: 02/13/2024]
Abstract
Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.
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Affiliation(s)
| | - Alejandro Moya-Esteban
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
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19
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Tahmid S, Font-Llagunes JM, Yang J. Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling. J Biomech Eng 2024; 146:011005. [PMID: 37902326 DOI: 10.1115/1.4063899] [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: 05/16/2023] [Accepted: 10/23/2023] [Indexed: 10/31/2023]
Abstract
Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29).
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Affiliation(s)
- Shadman Tahmid
- Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409
| | - Josep M Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona 08028, Catalonia, Spain; Health Technologies and Innovation, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat 08950, Catalonia, Spain
| | - James Yang
- Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409
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20
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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.
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21
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Caillet AH, Phillips ATM, Farina D, Modenese L. Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy. PLoS Comput Biol 2023; 19:e1011606. [PMID: 38060619 PMCID: PMC10729998 DOI: 10.1371/journal.pcbi.1011606] [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: 06/26/2023] [Revised: 12/19/2023] [Accepted: 10/16/2023] [Indexed: 12/20/2023] Open
Abstract
The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.
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Affiliation(s)
- Arnault H. Caillet
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Andrew T. M. Phillips
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Luca Modenese
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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22
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Feola E, Refai MIM, Costanzi D, Sartori M, Calanca A. A Neuromechanical Model-Based Strategy to Estimate the Operator's Payload in Industrial Lifting Tasks. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4644-4652. [PMID: 37983149 DOI: 10.1109/tnsre.2023.3334993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
One of the main technological barriers hindering the development of active industrial exoskeleton is today represented by the lack of suitable payload estimation algorithms characterized by high accuracy and low calibration time. The knowledge of the payload enables exoskeletons to dynamically provide the required assistance to the user. This work proposes a payload estimation methodology based on personalized Electromyography-driven musculoskeletal models (pEMS) combined with a payload estimation method we called "delta torque" that allows the decoupling of payload dynamical properties from human dynamical properties. The contribution of this work lies in the conceptualization of such methodology and its validation considering human operators during industrial lifting tasks. With respect to existing solutions often based on machine learning, our methodology requires smaller training datasets and can better generalize across different payloads and tasks. The proposed payload estimation methodology has been validated on lifting tasks with 0kg, 5kg, 10kg and 15kg, resulting in an average MAE of about 1.4 Kg. Even if 5kg and 10Kg lifting tasks were out of the training set, the MAE related to these tasks are 1.6 kg and 1.1 kg, respectively, demonstrating the generalizing property of the proposed methodology. To the best of the authors' knowledge, this is the first time that an EMG-driven model-based approach is proposed for human payload estimation.
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23
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Kizyte A, Lei Y, Wang R. Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4286-4294. [PMID: 37815967 DOI: 10.1109/tnsre.2023.3323364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.
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Karimi Dastgerdi A, Esrafilian A, Carty CP, Nasseri A, Yahyaiee Bavil A, Barzan M, Korhonen RK, Astori I, Hall W, Saxby DJ. Validation and evaluation of subject-specific finite element models of the pediatric knee. Sci Rep 2023; 13:18328. [PMID: 37884632 PMCID: PMC10603053 DOI: 10.1038/s41598-023-45408-5] [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: 07/20/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Finite element (FE) models have been widely used to investigate knee joint biomechanics. Most of these models have been developed to study adult knees, neglecting pediatric populations. In this study, an atlas-based approach was employed to develop subject-specific FE models of the knee for eight typically developing pediatric individuals. Initially, validation simulations were performed at four passive tibiofemoral joint (TFJ) flexion angles, and the resulting TFJ and patellofemoral joint (PFJ) kinematics were compared to corresponding patient-matched measurements derived from magnetic resonance imaging (MRI). A neuromusculoskeletal-(NMSK)-FE pipeline was then used to simulate knee biomechanics during stance phase of walking gait for each participant to evaluate model simulation of a common motor task. Validation simulations demonstrated minimal error and strong correlations between FE-predicted and MRI-measured TFJ and PFJ kinematics (ensemble average of root mean square errors < 5 mm for translations and < 4.1° for rotations). The FE-predicted kinematics were strongly correlated with published reports (ensemble average of Pearson's correlation coefficients (ρ) > 0.9 for translations and ρ > 0.8 for rotations), except for TFJ mediolateral translation and abduction/adduction rotation. For walking gait, NMSK-FE model-predicted knee kinematics, contact areas, and contact pressures were consistent with experimental reports from literature. The strong agreement between model predictions and experimental reports underscores the capability of sequentially linked NMSK-FE models to accurately predict pediatric knee kinematics, as well as complex contact pressure distributions across the TFJ articulations. These models hold promise as effective tools for parametric analyses, population-based clinical studies, and enhancing our understanding of various pediatric knee injury mechanisms. They also support intervention design and prediction of surgical outcomes in pediatric populations.
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Affiliation(s)
- Ayda Karimi Dastgerdi
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia.
| | - Amir Esrafilian
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
- Department of Orthopedics, Children's Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Alireza Yahyaiee Bavil
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Ivan Astori
- Department of Orthopedics, Children's Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | - Wayne Hall
- School of Engineering and Built Environment, Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia
| | - David John Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
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25
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Gogeascoechea A, Ornelas-Kobayashi R, Yavuz US, Sartori M. Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4040-4050. [PMID: 37756177 DOI: 10.1109/tnsre.2023.3319959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.
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26
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Konrath JM, Killen BA, Saxby DJ, Pizzolato C, Kennedy BA, Vertullo CJ, Barrett RS, Lloyd DG. Hamstring harvest results in significantly reduced knee muscular protection during side-step cutting two years after anterior cruciate ligament reconstruction. PLoS One 2023; 18:e0292867. [PMID: 37824493 PMCID: PMC10569629 DOI: 10.1371/journal.pone.0292867] [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: 02/22/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
The purpose of this study was to determine the effect of donor muscle morphology following tendon harvest in anterior cruciate ligament (ACL) reconstruction on muscular support of the tibiofemoral joint during sidestep cutting. Magnetic resonance imaging (MRI) was used to measure peak cross-sectional area (CSA) and volume of the semitendinosus (ST) and gracilis (GR) muscles and tendons (bilaterally) in 18 individuals following ACL reconstruction. Participants performed sidestep cutting tasks in a biomechanics laboratory during which lower-limb electromyography, ground reaction loads, whole-body motions were recorded. An EMG driven neuro-musculoskeletal model was subsequently used to determine force from 34 musculotendinous units of the lower limb and the contribution of the ST and GR to muscular support of the tibiofemoral joint based on a normal muscle-tendon model (Standard model). Then, differences in peak CSA and volume between the ipsilateral/contralateral ST and GR were used to adjust their muscle-tendon parameters in the model followed by a recalibration to determine muscle force for 34 musculotendinous units (Adjusted model). The combined contribution of the donor muscles to muscular support about the medial and lateral compartments were reduced by 52% and 42%, respectively, in the adjusted compared to standard model. While the semimembranosus (SM) increased its contribution to muscular stabilisation about the medial and lateral compartment by 23% and 30%, respectively. This computer simulation study demonstrated the muscles harvested for ACL reconstruction reduced their support of the tibiofemoral joint during sidestep cutting, while the SM may have the potential to partially offset these reductions. This suggests donor muscle impairment could be a factor that contributes to ipsilateral re-injury rates to the ACL following return to sport.
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Affiliation(s)
- Jason M. Konrath
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
- Principia Technology, Crawley, Western Australia, Australia
| | - Bryce A. Killen
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
| | - David J. Saxby
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
| | - Claudio Pizzolato
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
| | | | - Christopher J. Vertullo
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
- Knee Research Australia, Gold Coast, Queensland, Australia
| | - Rod S. Barrett
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
| | - David G. Lloyd
- School of Allied Health Sciences and Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
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27
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Kainz H, Mindler GT, Kranzl A. Influence of femoral anteversion angle and neck-shaft angle on muscle forces and joint loading during walking. PLoS One 2023; 18:e0291458. [PMID: 37824447 PMCID: PMC10569567 DOI: 10.1371/journal.pone.0291458] [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: 04/28/2023] [Accepted: 08/30/2023] [Indexed: 10/14/2023] Open
Abstract
Femoral deformities, e.g. increased or decreased femoral anteversion (AVA) and neck-shaft angle (NSA), can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how femoral geometry influences muscle forces and joint load during walking is still not fully understood. The objective of our study was to investigate the influence of femoral AVA and NSA on muscle forces and joint loads during walking. We conducted a comprehensive musculoskeletal modelling study based on three-dimensional motion capture data of a healthy person with a typical gait pattern. We created 25 musculoskeletal models with a variety of NSA (93°-153°) and AVA (-12°-48°). For each model we calculated moment arms, muscle forces, muscle moments, co-contraction indices and joint loads using OpenSim. Multiple regression analyses were used to predict muscle activations, muscle moments, co-contraction indices, and joint contact forces based on the femoral geometry. We found a significant increase in co-contraction of hip and knee joint spanning muscles in models with increasing AVA and NSA, which led to a substantial increase in hip and knee joint contact forces. Decreased AVA and NSA had a minor impact on muscle and joint contact forces. Large AVA lead to increases in both knee and hip contact forces. Large NSA (153°) combined with large AVA (48°) led to increases in hip joint contact forces by five times body weight. Low NSA (108° and 93°) combined with large AVA (48°) led to two-fold increases in the second peak of the knee contact forces. Increased joint contact forces in models with increased AVA and NSA were linked to changes in hip muscle moment arms and compensatory increases in hip and knee muscle forces. Knowing the influence of femoral geometry on muscle forces and joint loads can help clinicians to improve treatment strategies in patients with femoral deformities.
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Affiliation(s)
- Hans Kainz
- Centre for Sport Science and University Sports, Department of Biomechanics, Kinesiology and Computer Science in Sport, Neuromechanics Research Group, University of Vienna, Vienna, Austria
| | - Gabriel T. Mindler
- Department of Pediatric Orthopaedics, Orthopaedic Hospital Speising, Vienna, Austria
- Vienna Bone and Growth Center, Vienna, Austria
| | - Andreas Kranzl
- Vienna Bone and Growth Center, Vienna, Austria
- Laboratory for Gait and Movement Analysis, Orthopaedic Hospital Speising, Vienna, Austria
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28
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Romanato M, Zhang L, Sawacha Z, Gutierrez-Farewik EM. Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals. Clin Biomech (Bristol, Avon) 2023; 109:106074. [PMID: 37660576 DOI: 10.1016/j.clinbiomech.2023.106074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/20/2023] [Accepted: 08/17/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces. METHODS Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization. FINDINGS The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks. INTERPRETATION Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.
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Affiliation(s)
- M Romanato
- Department of Information Engineering, University of Padova, Padova, Italy
| | - L Zhang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Z Sawacha
- Department of Information Engineering, University of Padova, Padova, Italy; Department of Medicine, University of Padova, Padova, Italy.
| | - E M Gutierrez-Farewik
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
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29
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Saxby DJ, Pizzolato C, Diamond LE. A Digital Twin Framework for Precision Neuromusculoskeletal Health Care: Extension Upon Industrial Standards. J Appl Biomech 2023; 39:347-354. [PMID: 37567581 DOI: 10.1123/jab.2023-0114] [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: 04/29/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/13/2023]
Abstract
There is a powerful global trend toward deeper integration of digital twins into modern life driven by Industry 4.0 and 5.0. Defense, agriculture, engineering, manufacturing, and urban planning sectors have thoroughly incorporated digital twins to great benefit across their respective product lifecycles. Despite clear benefits, a digital twin framework for health and medical sectors is yet to emerge. This paper proposes a digital twin framework for precision neuromusculoskeletal health care. We build upon the International Standards Organization framework for digital twins for manufacturing by presenting best available computational models within a digital twin framework for clinical application. We map a use case for modeling Achilles tendon mechanobiology, highlighting how current modeling practices align with our proposed digital twin framework. Similarly, we map a use case for advanced neurorehabilitation technology, highlighting the role of a digital twin in control of systems where human and machine are interfaced. Future work must now focus on creating an informatic representation to govern how digital data are passed to, from, and within the digital twin, as well as specific standards to declare which measurement systems and modeling methods are acceptable to move toward widespread use of the digital twin framework for precision neuromusculoskeletal health care.
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Affiliation(s)
- David J Saxby
- Giffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Parklands,Australia
- School of Health Sciences and Social Work, Griffith University, Parklands,Australia
| | - Claudio Pizzolato
- Giffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Parklands,Australia
- School of Health Sciences and Social Work, Griffith University, Parklands,Australia
| | - Laura E Diamond
- Giffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Parklands,Australia
- School of Health Sciences and Social Work, Griffith University, Parklands,Australia
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30
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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.
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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
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31
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Mahdian ZS, Wang H, Refai MIM, Durandau G, Sartori M, MacLean MK. Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review. J Appl Biomech 2023; 39:318-333. [PMID: 37751903 DOI: 10.1123/jab.2023-0046] [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: 02/28/2023] [Revised: 08/11/2023] [Accepted: 08/18/2023] [Indexed: 09/28/2023]
Abstract
Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.
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Affiliation(s)
- Zahra S Mahdian
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Huawei Wang
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | | | - Guillaume Durandau
- Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Mhairi K MacLean
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
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32
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Uhlrich SD, Falisse A, Kidziński Ł, Muccini J, Ko M, Chaudhari AS, Hicks JL, Delp SL. OpenCap: Human movement dynamics from smartphone videos. PLoS Comput Biol 2023; 19:e1011462. [PMID: 37856442 PMCID: PMC10586693 DOI: 10.1371/journal.pcbi.1011462] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/24/2023] [Indexed: 10/21/2023] Open
Abstract
Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
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Affiliation(s)
- Scott D. Uhlrich
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Antoine Falisse
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Łukasz Kidziński
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Julie Muccini
- Radiology, Stanford University, Stanford, California, United States of America
| | - Michael Ko
- Radiology, Stanford University, Stanford, California, United States of America
| | - Akshay S. Chaudhari
- Radiology, Stanford University, Stanford, California, United States of America
- Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Jennifer L. Hicks
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Scott L. Delp
- Departments of Bioengineering, Stanford University, Stanford, California, United States of America
- Mechanical Engineering, Stanford University, Stanford, California, United States of America
- Orthopaedic Surgery, Stanford University, Stanford, California, United States of America
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Mathieu E, Crémoux S, Duvivier D, Amarantini D, Pudlo P. Biomechanical modeling for the estimation of muscle forces: toward a common language in biomechanics, medical engineering, and neurosciences. J Neuroeng Rehabil 2023; 20:130. [PMID: 37752507 PMCID: PMC10521397 DOI: 10.1186/s12984-023-01253-1] [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: 04/19/2022] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Different research fields, such as biomechanics, medical engineering or neurosciences take part in the development of biomechanical models allowing for the estimation of individual muscle forces involved in motor action. The heterogeneity of the terminology used to describe these models according to the research field is a source of confusion and can hamper collaboration between the different fields. This paper proposes a common language based on lexical disambiguation and a synthesis of the terms used in the literature in order to facilitate the understanding of the different elements of biomechanical modeling for force estimation, without questioning the relevance of the terms used in each field or the different model components or their interest. We suggest that the description should start with an indication of whether the muscle force estimation problem is solved following the physiological movement control (from the nervous drive to the muscle force production) or in the opposite direction. Next, the suitability of the model for force production estimation at a given time or for monitoring over time should be specified. Authors should pay particular attention to the method description used to find solutions, specifying whether this is done during or after data collection, with possible method adaptations during processing. Finally, the presence of additional data must be specified by indicating whether they are used to drive, assist, or calibrate the model. Describing and classifying models in this way will facilitate the use and application in all fields where the estimation of muscle forces is of real, direct, and concrete interest.
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Affiliation(s)
- Emilie Mathieu
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
| | - Sylvain Crémoux
- Centre de Recherche Cerveau et Cognition (CerCO), UMR CNRS 5549, Paul Sabatier University, Toulouse, France
| | - David Duvivier
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
| | - David Amarantini
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, Paul Sabatier University, Toulouse, France.
| | - Philippe Pudlo
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
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34
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Zhang L, Soselia D, Wang R, Gutierrez-Farewik EM. Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3722-3731. [PMID: 37708013 DOI: 10.1109/tnsre.2023.3315373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A cluster approach that grouped movements based on characteristic similarity was implemented, and its ability to improve the estimation accuracy of both NMS and LSTM models was evaluated. We compared torque estimation accuracy of NMS and LSTM models in three cases: Pooled, Individual, and Clustered models. Pooled models used data from all 10 movements to calibrate or train one model, Individual models used data from each individual movement, and Clustered models used data from each cluster. Individual, Clustered and Pooled LSTM models all had relatively high joint torque estimation accuracy. Individual and Clustered NMS models had similarly good estimation performance whereas the Pooled model may be too generic to satisfy all movement patterns. While the cluster approach improved the estimation accuracy in NMS models in some movements, it made relatively little difference in the LSTM neural networks, which already had high estimation accuracy. Our study provides practical implications for designing assist-as-needed exoskeleton controllers by offering guidelines for selecting the appropriate model for different scenarios, and has potential to enhance the functionality of wearable exoskeletons and improve rehabilitation and assistance for individuals with motor disorders.
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35
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Damonte F, Durandau G, Gonzalez-Vargas J, Van Der Kooij H, Sartori M. Synergy-Driven Musculoskeletal Modeling to Estimate Muscle Excitations and Joint Moments at Different Walking Speeds in Individuals with Transtibial Amputation. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941287 DOI: 10.1109/icorr58425.2023.10304814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
The main requirement for an amputee is to regain the function of the lost limb. In order to fully benefit from powered prosthetic legs, the user must rely on the dynamic control of the device. Progress in high-level control for powered prosthetic legs is currently challenged by the inability of current control schemes to generalize to large repertoires of movements as well as adapting to external mechanical demands. This ultimately leads the user to adopt compensatory movements, lack of comfort, higher energy requirements during walking and standing. This study uses a feedforward model of muscle activation and force generation that applies mathematical formulations of muscle synergies to generate synthetic activation profiles underlying walking across different speeds. Estimated activation profiles are used to drive forward subject-specific numerical models of the lower extremity musculoskeletal system. The model was validated on one individual with uni-lateral transtibial amputation and its predictions were compared to experimental torques from inverse dynamic calculations. Results showed that a generic muscle synergy driven personalized musculoskeletal model can fit the ankle torques of the intact limb of a person with transtibial amputation (RMSD = 0.1329±0.02). The estimated moments might be suitable as the control signal to drive powered prostheses to ultimately improve physical interaction between the user and a powered prostheses during dynamic motor tasks.
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Hambly MJ, De Sousa ACC, Lloyd DG, Pizzolato C. EMG-Informed Neuromusculoskeletal Modelling Estimates Muscle Forces and Joint Moments During Electrical Stimulation. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941242 DOI: 10.1109/icorr58425.2023.10304785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
This study implemented an electromyogram (EMG)-informed neuromusculoskeletal (NMS) model evaluating the volitional contributions to muscle forces and joint moments during functional electrical stimulation (FES). The NMS model was calibrated using motion and EMG (biceps brachii and triceps brachii) data recorded from able-bodied participants (n=3) performing weighted elbow flexion and extension cycling movements while equipped with an EMG-controlled closed-loop FES system. Models were executed using three computational approaches (i) EMG-driven, (ii) EMG-hybrid and (iii) EMG-assisted to estimate muscle forces and joint moments. Both EMG-hybrid and EMG-assisted modes were able estimate the elbow moment (root mean squared error and coefficient of determination), but the EMG-hybrid method also enabled quantifying the volitional contributions to muscle forces and elbow moments during FES. The proposed modelling method allows for assessing volitional contributions of patients to muscle force during FES rehabilitation, and could be used as biomarkers of recovery, biofeedback, and for real-time control of combined FES and robotic systems.
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Nabipour M, Sawicki GS, Sartori M. Predictive Control of Peak Achilles Tendon Force in a Simulated System of the Human Ankle Joint with a Parallel Artificial Actuator During Hopping. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941182 DOI: 10.1109/icorr58425.2023.10304771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Latest advances in wearable exoskeletons for the human lower extremity predominantly focus on minimising metabolic cost of walking. However, there currently is no robotic exoskeleton that gains control on the mechanics of biological tissues such as biological muscles or series-elastic tendons. Achieving robotic control of biological tissue mechanics would enable prevention of musculoskeletal injuries or the personalization of rehabilitation treatments following injury with levels of precisions not attained before. In this paper, we introduce a new framework that uses nonlinear model predictive control (NMPC) for the closed-loop control of peak tendon force in a simulated system of the human ankle joint with parallel exoskeletal actuation. We propose a computationally efficient NMPC's inner model consisting of explicit, closed-form equations of muscle-tendon dynamics along with those of the ankle joint with parallel actuation. The proposed formulation is tested and verified on movement data collected during dynamic ankle dorsiflexion/plantarflexion rotations executed on a dynamometer as well as during walking and running on a treadmill. The framework designed using the NMPC controller showed a promising performance in keeping the Achilles tendon force under a predefined threshold. Results indicated that our proposed model was generalizable to different muscles and gaits and suitable for real-time applications due to its low computational time.
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Lee J, Akbas T, Sulzer J. Hip and Knee Joint Kinematics Predict Quadriceps Hyperreflexia in People with Post-stroke Stiff-Knee Gait. Ann Biomed Eng 2023; 51:1965-1974. [PMID: 37133540 PMCID: PMC11003447 DOI: 10.1007/s10439-023-03217-x] [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: 11/11/2022] [Accepted: 04/20/2023] [Indexed: 05/04/2023]
Abstract
Wearable assistive technology for the lower extremities has shown great promise towards improving gait function in people with neuromuscular injuries. But common secondary impairments, such as hypersensitive stretch reflexes or hyperreflexia, have been often neglected. Incorporation of biomechanics into the control loop could improve individualization and avoid hyperreflexia. However, adding hyperreflexia prediction to the control loop would require expensive or complex measurement of muscle fiber characteristics. In this study, we explore a clinically accessible biomechanical predictor set that can accurately predict rectus femoris (RF) reaction after knee flexion assistance in pre-swing by a powered orthosis. We examined a total of 14 gait parameters based on gait kinematic, kinetic, and simulated muscle-tendon states from 8 post-stroke individuals with Stiff-Knee gait (SKG) wearing a knee exoskeleton robot. We independently performed both parametric and non-parametric variable selection approaches using machine learning regression techniques. Both models revealed the same four kinematic variables relevant to knee and hip joint motions were sufficient to effectively predict RF hyperreflexia. These results suggest that control of knee and hip kinematics may be a more practical method of incorporating quadriceps hyperreflexia into the exoskeleton control loop than the more complex acquisition of muscle fiber properties.
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Affiliation(s)
- Jeonghwan Lee
- Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
| | | | - James Sulzer
- Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center and Case Western Reserve University, Cleveland, OH, USA.
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Gaudio LA, Gonzalez-Vargas J, Sartori M, van der Kooij H. Subject-Specific and COM Acceleration-Enhanced Reflex Neuromuscular Model to Predict Ankle Responses in Perturbed Gait. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941200 DOI: 10.1109/icorr58425.2023.10304748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Subject-specific musculoskeletal models generate more accurate joint torque estimates from electromyography (EMG) inputs in relation to experimentally obtained torques. Similarly, reflex Neuromuscular Models (NMMs) that employ COM states in addition to musculotendon information generate muscle activations to musculoskeletal models that better predict ankle torques during perturbed gait. In this study, the reflex NMM of locomotion of one subject is identified by employing an EMG-calibrated musculoskeletal model in unperturbed and perturbed gait. A COM acceleration-enhanced reflex NMM is identified. Subject-specific musculoskeletal models improve torque tracking of the ankle joint in unperturbed and perturbed conditions. COM acceleration-enhanced reflex NMM improves ankle torque tracking especially in early stance and during backward perturbation. Results found herein can guide the implementation of reflex controllers in active prosthetic and orthotic devices.
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Zhang L, Zhang X, Zhu X, Wang R, Gutierrez-Farewik EM. Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification. Front Neurosci 2023; 17:1254088. [PMID: 37712095 PMCID: PMC10498472 DOI: 10.3389/fnins.2023.1254088] [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: 07/06/2023] [Accepted: 08/11/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. Methods To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Results Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. Discussion The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.
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Affiliation(s)
- Longbin Zhang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaochen Zhang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xueyu Zhu
- Department of Mathematics, University of Iowa, Iowa City, IA, United States
| | - Ruoli Wang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Elena M. Gutierrez-Farewik
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
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Moya-Esteban A, Durandau G, van der Kooij H, Sartori M. Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models. J Biomech 2023; 157:111727. [PMID: 37499430 DOI: 10.1016/j.jbiomech.2023.111727] [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: 02/10/2023] [Revised: 06/13/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023]
Abstract
Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.
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Affiliation(s)
- A Moya-Esteban
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.
| | - G Durandau
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
| | - H van der Kooij
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands; Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - M Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
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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.
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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.
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Meszaros-Beller L, Hammer M, Schmitt S, Pivonka P. Effect of neglecting passive spinal structures: a quantitative investigation using the forward-dynamics and inverse-dynamics musculoskeletal approach. Front Physiol 2023; 14:1135531. [PMID: 37324394 PMCID: PMC10264677 DOI: 10.3389/fphys.2023.1135531] [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: 01/01/2023] [Accepted: 04/28/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose: Inverse-dynamics (ID) analysis is an approach widely used for studying spine biomechanics and the estimation of muscle forces. Despite the increasing structural complexity of spine models, ID analysis results substantially rely on accurate kinematic data that most of the current technologies are not capable to provide. For this reason, the model complexity is drastically reduced by assuming three degrees of freedom spherical joints and generic kinematic coupling constraints. Moreover, the majority of current ID spine models neglect the contribution of passive structures. The aim of this ID analysis study was to determine the impact of modelled passive structures (i.e., ligaments and intervertebral discs) on remaining joint forces and torques that muscles must balance in the functional spinal unit. Methods: For this purpose, an existing generic spine model developed for the use in the demoa software environment was transferred into the musculoskeletal modelling platform OpenSim. The thoracolumbar spine model previously used in forward-dynamics (FD) simulations provided a full kinematic description of a flexion-extension movement. By using the obtained in silico kinematics, ID analysis was performed. The individual contribution of passive elements to the generalised net joint forces and torques was evaluated in a step-wise approach increasing the model complexity by adding individual biological structures of the spine. Results: The implementation of intervertebral discs and ligaments has significantly reduced compressive loading and anterior torque that is attributed to the acting net muscle forces by -200% and -75%, respectively. The ID model kinematics and kinetics were cross-validated against the FD simulation results. Conclusion: This study clearly shows the importance of incorporating passive spinal structures on the accurate computation of remaining joint loads. Furthermore, for the first time, a generic spine model was used and cross-validated in two different musculoskeletal modelling platforms, i.e., demoa and OpenSim, respectively. In future, a comparison of neuromuscular control strategies for spinal movement can be investigated using both approaches.
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Affiliation(s)
- Laura Meszaros-Beller
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
| | - Maria Hammer
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Syn Schmitt
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Peter Pivonka
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
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Kaneda JM, Seagers KA, Uhlrich SD, Kolesar JA, Thomas KA, Delp SL. Can static optimization detect changes in peak medial knee contact forces induced by gait modifications? J Biomech 2023; 152:111569. [PMID: 37058768 PMCID: PMC10231980 DOI: 10.1016/j.jbiomech.2023.111569] [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: 07/10/2022] [Revised: 02/13/2023] [Accepted: 03/24/2023] [Indexed: 03/28/2023]
Abstract
Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.
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Affiliation(s)
- Janelle M Kaneda
- Department of Bioengineering, Stanford University, Stanford, CA, United States.
| | - Kirsten A Seagers
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Julie A Kolesar
- Department of Bioengineering, Stanford University, Stanford, CA, United States; Musculoskeletal Research Lab, VA Palo Alto Healthcare System, Palo Alto, CA, United States
| | - Kevin A Thomas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Scott L Delp
- Department of Bioengineering, Stanford University, Stanford, CA, United States; Department of Mechanical Engineering, Stanford University, Stanford, CA, United States; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, United States
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Lloyd DG, Saxby DJ, Pizzolato C, Worsey M, Diamond LE, Palipana D, Bourne M, de Sousa AC, Mannan MMN, Nasseri A, Perevoshchikova N, Maharaj J, Crossley C, Quinn A, Mulholland K, Collings T, Xia Z, Cornish B, Devaprakash D, Lenton G, Barrett RS. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport 2023:S1440-2440(23)00070-1. [PMID: 37149408 DOI: 10.1016/j.jsams.2023.04.001] [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: 05/31/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. DESIGN Narrative review. METHODS Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. RESULTS Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. CONCLUSIONS Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Matthew Worsey
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Dinesh Palipana
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Medicine, Dentistry and Health, Griffith University, Australia
| | - Matthew Bourne
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana Cardoso de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Malik Muhammad Naeem Mannan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Nataliya Perevoshchikova
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Jayishni Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claire Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Alastair Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Kyle Mulholland
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Tyler Collings
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Bradley Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; VALD Performance, Australia
| | | | - Rodney S Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
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Collings TJ, Bourne MN, Barrett RS, Meinders E, GONçALVES BASAM, Shield AJ, Diamond LE. Gluteal Muscle Forces during Hip-Focused Injury Prevention and Rehabilitation Exercises. Med Sci Sports Exerc 2023; 55:650-660. [PMID: 36918403 DOI: 10.1249/mss.0000000000003091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
PURPOSE This study aimed to compare and rank gluteal muscle forces in eight hip-focused exercises performed with and without external resistance and describe the underlying fiber lengths, velocities, and muscle activations. METHODS Motion capture, ground reaction forces, and electromyography (EMG) were used as input to an EMG-informed neuromusculoskeletal model to estimate gluteus maximus, medius, and minimus muscle forces. Participants were 14 female footballers (18-32 yr old) with at least 3 months of lower limb strength training experience. Each participant performed eight hip-focused exercises (single-leg squat, split squat, single-leg Romanian deadlift [RDL], single-leg hip thrust, banded side step, hip hike, side plank, and side-lying leg raise) with and without 12 repetition maximum (RM) resistance. For each muscle, exercises were ranked by peak muscle force, and k-means clustering separated exercises into four tiers. RESULTS The tier 1 exercises for gluteus maximus were loaded split squat (95% confidence interval [CI] = 495-688 N), loaded single-leg RDL (95% CI = 500-655 N), and loaded single-leg hip thrust (95% CI = 505-640 N). The tier 1 exercises for gluteus medius were body weight side plank (95% CI = 338-483 N), loaded single-leg squat (95% CI = 278-422 N), and loaded single-leg RDL (95% CI = 283-405 N). The tier 1 exercises for gluteus minimus were loaded single-leg RDL (95% CI = 267-389 N) and body weight side plank (95% CI = 272-382 N). Peak gluteal muscle forces increased by 28-150 N when exercises were performed with 12RM external resistance compared with body weight only. Peak muscle force coincided with maximum fiber length for most exercises. CONCLUSIONS Gluteal muscle forces were exercise specific, and peak muscle forces increased by varying amounts when adding a 12RM external resistance. These findings may inform exercise selection by facilitating the targeting of individual gluteal muscles and optimization of mechanical loads to match performance, injury prevention, or rehabilitation training goals.
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Bu A, MacLean MK, Ferris DP. EMG-informed neuromuscular model assesses the effects of varied bodyweight support on muscles during overground walking. J Biomech 2023; 151:111532. [PMID: 36906966 PMCID: PMC10050108 DOI: 10.1016/j.jbiomech.2023.111532] [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: 04/14/2022] [Revised: 02/24/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023]
Abstract
Bodyweight supported walking is a common gait rehabilitation method that can be used as an experimental approach to better understand walking biomechanics. Neuromuscular modeling can provide an analytical means to gain insight into how muscles coordinate to produce walking and other movements. To better understand how muscle length and velocity affect muscle force during overground walking with bodyweight support, we used an electromyography (EMG)-informed neuromuscular model to investigate changes in muscle parameters (muscle force, activation and fiber length) at varying bodyweight support levels: 0%, 24%, 45% and 69% bodyweight. Coupled constant force springs provided a vertical support force while we collected biomechanical data (EMG, motion capture and ground reaction forces) from healthy, neurologically intact participants walking at 1.20 ± 0.06 m/s. The lateral and medial gastrocnemius demonstrated a significant decrease in muscle force (lateral: p = 0.002 and medial: p < 0.001) and activation (lateral: p = 0.007 and medial: p < 0.001) through push-off at higher levels of support. The soleus, in contrast, had no significant change in muscle activation through push-off (p = 0.652) regardless of bodyweight support level even though soleus muscle force decreased with increasing support (p < 0.001). During push-off, the soleus had shorter muscle fiber lengths and faster shortening velocities as bodyweight support levels increased. These results provide insight into how muscle force can be decoupled from effective bodyweight during bodyweight supported walking due to changes in muscle fiber dynamics. The findings contribute evidence that clinicians and biomechanists should not expect a reduction in muscle activation and force when using bodyweight support to assist gait during rehabilitation.
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Affiliation(s)
- Angel Bu
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Mhairi K MacLean
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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Moghadam SM, Yeung T, Choisne J. A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors. Sci Rep 2023; 13:5046. [PMID: 36977706 PMCID: PMC10049990 DOI: 10.1038/s41598-023-31906-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.
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Affiliation(s)
| | - Ted Yeung
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Julie Choisne
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
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Nasseri A, Akhundov R, Bryant AL, Lloyd DG, Saxby DJ. Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces. Bioengineering (Basel) 2023; 10:bioengineering10030369. [PMID: 36978760 PMCID: PMC10045248 DOI: 10.3390/bioengineering10030369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/09/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Neuromusculoskeletal models often require three-dimensional (3D) body motions, ground reaction forces (GRF), and electromyography (EMG) as input data. Acquiring these data in real-world settings is challenging, with barriers such as the cost of instruments, setup time, and operator skills to correctly acquire and interpret data. This study investigated the consequences of limiting EMG and GRF data on modelled anterior cruciate ligament (ACL) forces during a drop–land–jump task in late-/post-pubertal females. We compared ACL forces generated by a reference model (i.e., EMG-informed neural mode combined with 3D GRF) to those generated by an EMG-informed with only vertical GRF, static optimisation with 3D GRF, and static optimisation with only vertical GRF. Results indicated ACL force magnitude during landing (when ACL injury typically occurs) was significantly overestimated if only vertical GRF were used for either EMG-informed or static optimisation neural modes. If 3D GRF were used in combination with static optimisation, ACL force was marginally overestimated compared to the reference model. None of the alternative models maintained rank order of ACL loading magnitudes generated by the reference model. Finally, we observed substantial variability across the study sample in response to limiting EMG and GRF data, indicating need for methods incorporating subject-specific measures of muscle activation patterns and external loading when modelling ACL loading during dynamic motor tasks.
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Affiliation(s)
- Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, QLD 4222, Australia
- Correspondence:
| | - Riad Akhundov
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, QLD 4222, Australia
| | - Adam L. Bryant
- Centre for Health, Exercise & Sports Medicine, University of Melbourne, Melbourne, VIC 3010, Australia
| | - David G. Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, QLD 4222, Australia
| | - David J. Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, QLD 4222, Australia
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50
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Saxby DJ, Catelli DS, Lloyd DG, Sawacha Z. Editorial: The role of biomechanics in anterior cruciate ligament injuries prevention. Front Sports Act Living 2023; 5:1134969. [PMID: 36969959 PMCID: PMC10034354 DOI: 10.3389/fspor.2023.1134969] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/06/2023] [Indexed: 03/11/2023] Open
Affiliation(s)
- David J. Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Danilo S. Catelli
- Department of Movement Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - David G. Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Zimi Sawacha
- Department of Information Engineering, University of Padua, Padua, Veneto, Italy
- Department of Medicine, University of Padua, Padua, Veneto, Italy
- Correspondence: Zimi Sawacha
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