1. Estimation of Upper-Limb Joint Torques in Static and Dynamic Phases for Lifting Tasks
- Author
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Ouadoudi Belabzioui, Hasnaa, Pontonnier, Charles, Dumont, Georges, Plantard, Pierre, Multon, Franck, Université de Rennes (UR), Analysis-Synthesis Approach for Virtual Human Simulation (MIMETIC), Université de Rennes 2 (UR2)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Moovency [Cesson Sévigné], and CIFRE funded by the Moovency company
- Subjects
normalization ,machine learning ,neural networks normalization regression problem inverse dynamics sequential data machine learning ,[SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph] ,inverse dynamics ,sequential data ,neural networks ,regression problem ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
International audience; In this paper, we propose learning architectures to emulate the inverse dynamics step in motion analysis. Indeed, the in situ motion analysis of a work situation is often based on noisy and/or incomplete motion data (video, depth camera...), requiring the development of methods robust to these uncertainties. Our study focuses on the development and evaluation on reference data (opto-electronic motion capture) of a torque estimation tool for upper limbs. The system was trained to estimate joint torques for static and dynamic one-handed load carrying tasks, based on the estimated position of the joint centers, the mass carried and the mass of the subject. The generalizability of our learning models was tested in inter-subject and inter-task scenarios. The average RMSE (N.m) and the average nRMSE (%) metrics were computed for each type of learning architecture. In a future work, we aim at emulating noisy data as an input of the problem to emulate in situ conditions and improve the robustness of the approach.
- Published
- 2023