Ison, Mark (Arizona State University), Artemiadis, Panagiotis (Arizona State University)

Beyond User-Specificity for EMG Decoding Using Multiresolution Muscle Synergy Analysis

Scheduled for presentation during the Contributed session "Bio-medical and Bio-mechanical Systems" (TuBT5), Tuesday, October 22, 2013, 14:10−14:30, Tent B

6th Annual Dynamic Systems and Control Conference, October 21-23, 2020, Stanford University, Munger Center, Palo Alto, CA

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

Electromyographic (EMG) processing is a vital step towards converting noisy muscle activation signals into robust features that can be decoded and applied to applications such as prosthetics, exoskeletons, and human-machine interfaces. Current state of the art processing methods involve collecting a dense set of features which are sensitive to many of the intra- and inter- subject variability ubiquitous in EMG signals. As a result, state of the art decoding methods have been unable to obtain subject independence. This paper presents a novel multiresolution muscle synergy (MRMS) feature extraction technique which represents a set of EMG signals in a sparse domain robust to the inherent variability of EMG signals. The robust features, which can be extracted in real time, are used to train a neural network and demonstrate a highly accurate and user-independent classifier. Leave-one-out validation testing achieves mean accuracy of 81.9 and area under the receiver operating characteristic curve (AUC), a measure of overall classifier performance over all possible thresholds, of 92.4. The results show the ability of sparse MRMS features to achieve subject independence in decoders, providing opportunities for large-scale studies and more robust EMG-driven applications.