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Probabilistic models for prosthetic control based on EMG decomposition

Konstantin Akhmadeev 1, 2, 3
2 ReV - Robotique Et Vivant
LS2N - Laboratoire des Sciences du Numérique de Nantes
3 SIMS - Signal, IMage et Son
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Modern prosthetic control can be significantly enhanced due to the use of EMG decomposition. This technique permits to extract the activity of motor neurons that control the movement, thus giving a direct representation of neural command. This activity, being unaltered by factors non-related to motion, such as type and position of EMG electrode, is of great interest in prosthetic control. Existing real-time decomposition methods, however, provide activities of a very limited number of motor neurons (up to ten). This can be considered insufficient for intent inference. In this work, we present a probabilistic approach to intent inference that uses existing models of relations between the behavior of motor neurons and the movement. We compare our approach with a conventional one presented in the literature and show that it produces significantly better results when provided with a small number of decomposed motor neurons. To assess its performance in a fully controlled environment, we have developed a physiology-based simulation model of EMG and muscle contraction. Moreover, the analysis was also performed using experimental recordings of muscle contractions.
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Submitted on : Tuesday, December 24, 2019 - 12:21:44 PM
Last modification on : Wednesday, January 19, 2022 - 3:48:25 PM
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K. Akhmadeev - Modèles proba...
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  • HAL Id : tel-02423435, version 1


Konstantin Akhmadeev. Probabilistic models for prosthetic control based on EMG decomposition. Signal and Image Processing. Université de Nantes, 2019. English. ⟨tel-02423435⟩



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