000132181 001__ 132181
000132181 005__ 20260217205454.0
000132181 0247_ $$2doi$$a10.1007/s11044-023-09938-0
000132181 0248_ $$2sideral$$a137502
000132181 037__ $$aART-2024-137502
000132181 041__ $$aeng
000132181 100__ $$aLugrís, Urbano
000132181 245__ $$aHuman motion capture, reconstruction, and musculoskeletal analysis in real time
000132181 260__ $$c2024
000132181 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132181 5203_ $$aOptical 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.
000132181 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PGC2018-095145-B-I00
000132181 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000132181 590__ $$a2.4$$b2024
000132181 592__ $$a0.868$$b2024
000132181 591__ $$aMECHANICS$$b79 / 171 = 0.462$$c2024$$dQ2$$eT2
000132181 593__ $$aAerospace Engineering$$c2024$$dQ1
000132181 593__ $$aControl and Optimization$$c2024$$dQ1
000132181 593__ $$aModeling and Simulation$$c2024$$dQ1
000132181 593__ $$aMechanical Engineering$$c2024$$dQ1
000132181 593__ $$aComputer Science Applications$$c2024$$dQ2
000132181 594__ $$a5.4$$b2024
000132181 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000132181 700__ $$aPérez-Soto, Manuel$$uUniversidad de Zaragoza
000132181 700__ $$aMichaud, Florian
000132181 700__ $$aCuadrado, Javier
000132181 7102_ $$15002$$2720$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Proyectos de Ingeniería
000132181 773__ $$g60, 1 (2024), 3-25$$pMultibody syst. dyn.$$tMULTIBODY SYSTEM DYNAMICS$$x1384-5640
000132181 8564_ $$s2201070$$uhttps://zaguan.unizar.es/record/132181/files/texto_completo.pdf$$yVersión publicada
000132181 8564_ $$s1380265$$uhttps://zaguan.unizar.es/record/132181/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000132181 909CO $$ooai:zaguan.unizar.es:132181$$particulos$$pdriver
000132181 951__ $$a2026-02-17-20:19:58
000132181 980__ $$aARTICLE