Abstract
Monitoring workers’ status is crucial to prevent work-related musculoskeletal disorders and to enable a safe human-robot interaction. This is typically achieved relying on muscle activation recordings, commonly performed via wearable electromyographic EMG sensors. However, to properly acquire whole-body muscular status, a large number of sensors is needed. This represents a limitation for a real deployment of wearable acquisition systems, due to cost and wearability constraints. To overcome this problem, we propose a solution to provide a reliable muscles estimation from a limited number of EMG recordings. Our method exploits the covariation patterns between muscles activation to complement the recordings coming from a reduced set of optimally placed sensors, minimizing the estimation uncertainty. Using a dataset of EMG data recorded from 10 subjects, we demonstrate that it is possible to reconstruct the temporal evolution of 10 whole-body muscles with a maximum normalized estimation error of 13%, using only 7 EMG sensors.
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Acknowledgements
This paper is part of a project that has received funding from European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No. 101017274 (DARKO), and Grant Agreement No. 871237 (SOPHIA). The research leading to these results has received partial funding also from the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence), and in the framework of PRIN (Programmi di Ricerca Scientifica di Rilevante Interesse Nazionale) 2017, with the project TIGHT: Tactile InteGration for Humans and arTificial systems, grant number 2017SB48FP.
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Baracca, M., Averta, G., Bianchi, M. (2023). Estimation of Whole-Body Muscular Activation from an Optimal Set of Scarce Electromyographic Recordings. In: Borja, P., Della Santina, C., Peternel, L., Torta, E. (eds) Human-Friendly Robotics 2022. HFR 2022. Springer Proceedings in Advanced Robotics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-031-22731-8_9
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