Deep learning for enhanced prosthetic control: Real-time motor intent decoding for simultaneous control of artificial limbs

Abstract

<p>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 traditional machine learning approaches, 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.</p>

Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

“…Previously, standard machine learning algorithms like support vector machine (SVM) and linear discriminant analysis (LDA) were used on the computational hardware to decode motion intent [1]. Due to the availability of bigger motion intent data sets and the need for higher performance, the field shifted towards neural networks (NN), especially deep neural networks [2]. Deep learning techniques like deep neural networks enable more complex and more accurate motion intent recognition [11].…”