Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs
Artikel i vetenskaplig tidskrift, 2024

The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able-bodied participants and an individual with an amputation. Our results demonstrate that deep learning architectures outperform shallow networks in decoding motor intent, with representation learning effectively extracting underlying motor control information from EMG signals. Furthermore, the observed performance improvements by using deep neural networks were consistent across both able-bodied and amputee participants. By employing deep neural networks instead of a shallow network, more reliable and precise control of a prosthesis can be achieved, which has the potential to significantly enhance prosthetic functionality and improve the quality of life for individuals with amputations.

prosthetics

simultaneous control

pattern recognition

prostheses

Deep learning

myoelectric

Författare

Jan Zbinden

Chalmers, Elektroteknik, System- och reglerteknik

Center for Bionics and Pain Research

Julia Molin

Meta

Center for Bionics and Pain Research

Max Jair Ortiz Catalan

Prometei Pain Rehabilitation Center

Bionics Institute

Center for Bionics and Pain Research

Chalmers, Elektroteknik, System- och reglerteknik

University of Melbourne

IEEE Transactions on Neural Systems and Rehabilitation Engineering

1534-4320 (ISSN) 1558-0210 (eISSN)

Vol. 32 1177-1186

Ämneskategorier

Robotteknik och automation

DOI

10.1109/TNSRE.2024.3371896

PubMed

38421839

Mer information

Senast uppdaterat

2024-03-27