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Article

Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables

Centre for Robotics, MINES ParisTech, PSL Université, 75006 Paris, France
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Academic Editor: Zdeněk Svoboda
Sensors 2021, 21(7), 2497; https://doi.org/10.3390/s21072497
Received: 15 January 2021 / Revised: 18 February 2021 / Accepted: 26 March 2021 / Published: 3 April 2021
(This article belongs to the Special Issue Sensors for Human Movement Applications)
In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture. View Full-Text
Keywords: movement modeling; state-space representation; gesture recognition; wearable sensors; ergonomics movement modeling; state-space representation; gesture recognition; wearable sensors; ergonomics
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MDPI and ACS Style

Olivas-Padilla, B.E.; Manitsaris, S.; Menychtas, D.; Glushkova, A. Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables. Sensors 2021, 21, 2497. https://doi.org/10.3390/s21072497

AMA Style

Olivas-Padilla BE, Manitsaris S, Menychtas D, Glushkova A. Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables. Sensors. 2021; 21(7):2497. https://doi.org/10.3390/s21072497

Chicago/Turabian Style

Olivas-Padilla, Brenda E., Sotiris Manitsaris, Dimitrios Menychtas, and Alina Glushkova. 2021. "Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables" Sensors 21, no. 7: 2497. https://doi.org/10.3390/s21072497

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