Estimating Biological Stiffness Without Relying on External Joint Perturbations: A Musculoskeletal Modeling Framework DOI
Christopher P. Cop

Опубликована: Сен. 11, 2023

In vivo joint stiffness estimation during time-varying conditions remains an open challenge.Multiple communities, e.g., system identification and biomechanics, have tackled the problem from different perspectives using methods, each of which entailing advantages limitations, often complementary.System formulations provide data-driven estimates at level, while biomechanics relies on musculoskeletal models to estimate multiple levels, i.e., joint, muscle, tendon.Collaboration across these two scientific communities seems be a logical step towards reliable multilevel understanding stiffness.However, differences theoretical, computational, experimental levels limited inter-community interaction.In this chapter we present roadmap achieve unified framework for in composite human neuromusculoskeletal movement.We our perspective future developments obtain that are compatible levels.Moreover, propose novel combined closed-loop paradigm, reference via decomposed into underlying muscle tendon contribution high-density-electromyography-driven modeling.We highlight need aligning requirements able compare both formulations.Unifying biomechanics' identification's is necessary truly generalizing individuals, movement conditions, training impairment levels.From application point view, central enabling patientspecific neurorehabilitation therapies, as well biomimetic control assistive robotic technologies.The could serve inspiration collaborations broadly understand bio-and neuromechanics.

Язык: Английский

Machine Learning for the Prediction of the Index of Effectiveness in Cycling DOI

A Torres,

Mario Yepez,

Geoffrey Millour

и другие.

Springer optimization and its applications, Год журнала: 2025, Номер unknown, С. 51 - 89

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video DOI Creative Commons
Zhengliang Xia, Bradley M. Cornish, Daniel Devaprakash

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2024, Номер 32, С. 2070 - 2077

Опубликована: Янв. 1, 2024

Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, complex experimental setup required to perform analyses confines use laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data predict during walking, running, countermovement jump, single-leg landing, and heel rise. The LSTM models were trained on pose estimation keypoints corresponding from 16 subjects, calculated via an established NMSK modeling pipeline, cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion one participant was collected two smartphones used forces. predicted time-series synthesized root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) 0.21, R 2 ≥ 0.81. Walking task resulted most accurate = 189±62 N; nRMSE 0.11±0.03, 0.92±0.04. physiologically plausible, agreeing in timing magnitude profiles. This study demonstrated feasibility low-cost solutions deploy biomechanical outside

Язык: Английский

Процитировано

2
Neuromusculoskeletal modeling in health and disease DOI Open Access
Hans Kainz, Antoine Falisse, Claudio Pizzolato

и другие.

Brazilian Journal of Motor Behavior, Год журнала: 2024, Номер 18

Опубликована: Апрель 27, 2024

This opinion paper provides an overview of musculoskeletal modeling, revealing insights into muscle-tendon kinematics, forces, and joint contact forces during dynamic movements, thereby advancing our understanding biomechanics. While subject-specific modeling poses challenges, emerging software tools enable rapid personalization geometry motor control, enhancing physiological accuracy. Advanced predictive simulations multi-scale expand clinical applications, facilitating surgery outcomes prediction movement modification for diseases. Collaborative interdisciplinary efforts are essential overcoming refining workflows, ultimately treatment outcomes.

Язык: Английский

Процитировано

1
Real-time calibration-free musculotendon kinematics for neuromusculoskeletal models DOI Creative Commons
Bradley M. Cornish, Laura E. Diamond, David J. Saxby

и другие.

Опубликована: Фев. 27, 2024

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part NMS modelling involves estimating musculotendon kinematics, which comprise unit lengths, moment arms, and lines action. Musculotendon are partially dependent on joint motions, define the non-linear mapping muscle forces to moments contact forces. Currently, real-time computation kinematics requires creation a per-individual surrogate model. The computational speed accuracy these surrogates degrade with increasing number coordinates. We developed feed-forward neural network that completely encodes target model across wide anthropometric range, enabling accurate estimates without need for priori Compared reference, had median normalized errors ~0.1% <0.4% <0.10° line action orientations. was employed within an electromyography-informed calculate hip forces, demonstrating little difference (normalized root mean square error 1.23±0.15%) compared using reference kinematics. Finally, execution time <0.04 ms per frame constant Our approach musculoskeletal may facilitate deployment complex in computer vision or wearable sensors applications realize biomechanics monitoring, rehabilitation, disease management outside research laboratory.

Язык: Английский

Процитировано

0
Sound of synergy: ultrasound and artificial intelligence in sports medicine DOI
Steven Duhig, Alec McKenzie

British Journal of Sports Medicine, Год журнала: 2024, Номер 58(16), С. 887 - 888

Опубликована: Май 2, 2024

Язык: Английский

Процитировано

0
Real-time calibration-free musculotendon kinematics for neuromusculoskeletal models DOI Creative Commons
Bradley M. Cornish, Laura E. Diamond, David J. Saxby

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2024, Номер 32, С. 3486 - 3495

Опубликована: Янв. 1, 2024

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part NMS modelling is the musculotendon kinematics, which comprise unit lengths, moment arms, and lines action. Musculotendon are partially dependent on joint angles, define non-linear mapping muscle forces to moments contact forces. Currently, real-time computation kinematics requires creation a per-individual surrogate model. The computational speed accuracy these surrogates degrade with increasing number coordinates. We developed feed-forward neural network that completely encodes target model across wide anthropometric range, enabling accurate estimates without need for priori Compared reference, had median normalized errors ~0.1% <0.4% <0.10° line action orientations. was employed within an electromyogram-informed calculate hip forces, demonstrating little difference (normalized root mean square error 1.23±0.15 %) compared using reference kinematics. Finally, execution time <0.04 ms per frame constant Our approach musculoskeletal may facilitate deployment complex in computer vision or wearable sensors applications realize biomechanics monitoring, rehabilitation, disease management outside research laboratory.

Язык: Английский

Процитировано

0
Estimating Biological Stiffness Without Relying on External Joint Perturbations: A Musculoskeletal Modeling Framework DOI
Christopher P. Cop

Опубликована: Сен. 11, 2023

In vivo joint stiffness estimation during time-varying conditions remains an open challenge.Multiple communities, e.g., system identification and biomechanics, have tackled the problem from different perspectives using methods, each of which entailing advantages limitations, often complementary.System formulations provide data-driven estimates at level, while biomechanics relies on musculoskeletal models to estimate multiple levels, i.e., joint, muscle, tendon.Collaboration across these two scientific communities seems be a logical step towards reliable multilevel understanding stiffness.However, differences theoretical, computational, experimental levels limited inter-community interaction.In this chapter we present roadmap achieve unified framework for in composite human neuromusculoskeletal movement.We our perspective future developments obtain that are compatible levels.Moreover, propose novel combined closed-loop paradigm, reference via decomposed into underlying muscle tendon contribution high-density-electromyography-driven modeling.We highlight need aligning requirements able compare both formulations.Unifying biomechanics' identification's is necessary truly generalizing individuals, movement conditions, training impairment levels.From application point view, central enabling patientspecific neurorehabilitation therapies, as well biomimetic control assistive robotic technologies.The could serve inspiration collaborations broadly understand bio-and neuromechanics.

Язык: Английский

Процитировано

0