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.
Complete list of metadatas

Cited literature [173 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/tel-02423435
Contributor : Konstantin Akhmadeev <>
Submitted on : Tuesday, December 24, 2019 - 12:21:44 PM
Last modification on : Monday, January 13, 2020 - 1:08:48 AM

File

K. Akhmadeev - Modèles proba...
Files produced by the author(s)

Identifiers

  • HAL Id : tel-02423435, version 1

Citation

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

Share

Metrics

Record views

22

Files downloads

46