Journal Article

Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth

Sager, Paulina, Andersson, Elin, Olsson, Alexander E., Björkman, Anders, Antfolk, Christian, Malešević, Nebojša

2019

Overview

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/378/116/2396 / 639/166/985 / 692/308/575 / Adult / Algorithms / Artificial Limbs / Electromyography / Electromyography - methods / Engineering and Technology / Female / Forearm / Forearm - physiology / Hand - physiology / Humanities and Social Sciences / Humans / Interfaces / Learning algorithms / Machine Learning / Male / Medical Engineering / Medical Materials (including Prosthesis technologies) / Medicinsk materialteknik (Här ingår: Protesteknik) / Medicinteknik / Middle Aged / Movement - physiology / multidisciplinary / Muscles - physiology / Neural networks / Neural Networks, Computer / Prosthetics / Science / Science (multidisciplinary) / Signal Processing, Computer-Assisted / Teknik