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Human motion capture, reconstruction, and musculoskeletal analysis in real time
dc.contributor.author | Lugrís-Armesto, Urbano | |
dc.contributor.author | Pérez Soto, Manuel | |
dc.contributor.author | Michaud, Florian | |
dc.contributor.author | Cuadrado, Javier | |
dc.date.accessioned | 2024-07-29T08:57:59Z | |
dc.date.available | 2024-07-29T08:57:59Z | |
dc.date.issued | 2023-10-06 | |
dc.identifier.citation | Lugrís, U., Pérez-Soto, M., Michaud, F. et al. Human motion capture, reconstruction, and musculoskeletal analysis in real time. Multibody Syst Dyn 60, 3–25 (2024). https://doi.org/10.1007/s11044-023-09938-0 | es_ES |
dc.identifier.issn | 1384-5640 | |
dc.identifier.issn | 1573-272X | |
dc.identifier.uri | http://hdl.handle.net/2183/38284 | |
dc.description.abstract | [Abstract] Optical motion capture is an essential tool for the study and analysis of human movement. Currently, most manufacturers of motion-capture systems provide software applications for reconstructing the movement in real time, thus allowing for on-the-fly visualization. The captured kinematics can be later used as input data for a further musculoskeletal analysis. However, in advanced biofeedback applications, the results of said analysis, such as joint torques, ground-reaction forces, muscle efforts, and joint-reaction forces, are also required in real time. In this work, an extended Kalman filter (EKF) previously developed by the authors for real-time, whole-body motion capture and reconstruction is augmented with inverse dynamics and muscle-efforts optimization, enabling the calculation and visualization of the latter, along with joint-reaction forces, while capturing the motion. A modified version of the existing motion-capture algorithm provides the positions, velocities, and accelerations at every time step. Then, the joint torques are calculated by solving the inverse-dynamics problem, using force-plate measurements along with previously estimated body-segment parameters. Once the joint torques are obtained, an optimization problem is solved, in order to obtain the muscle forces that provide said torques while minimizing an objective function. This is achieved by a very efficient quadratic programming algorithm, thoroughly tuned for this specific problem. With this procedure, it is possible to capture and label the optical markers, reconstruct the motion of the model, solve the inverse dynamics, and estimate the individual muscle forces, all while providing real-time visualization of the results. | es_ES |
dc.description.sponsorship | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095145-B-I00/ES/ESTUDIO DE LA RELACION ENTRE EFICIENCIA Y NIVEL DE DETALLE EN MODELOS BIOMECANICOS DEL CUERPO HUMANO | es_ES |
dc.description.sponsorship | Moreover, F.M. would like to acknowledge the support of the Galician Government and the Ferrol Industrial Campus by means of the postdoctoral research contract 2022/CP/048 | es_ES |
dc.description.sponsorship | Xunta de Galicia; 2022/CP/048 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11044-023-09938-0 | es_ES |
dc.rights | Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Biomechanics | es_ES |
dc.subject | Motion capture | es_ES |
dc.subject | Musculoskeletal analysis | es_ES |
dc.subject | Biofeedback | es_ES |
dc.subject | Kalman filter | es_ES |
dc.title | Human motion capture, reconstruction, and musculoskeletal analysis in real time | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Multibody System Dynamics | es_ES |
UDC.volume | 60 | es_ES |
UDC.startPage | 3 | es_ES |
UDC.endPage | 25 | es_ES |
dc.identifier.doi | https://doi.org/10.1007/s11044-023-09938-0 |
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