EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial.
@article{arxiv.1802.10237,
title = {An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier},
author = {Ali Moin and Andy Zhou and Abbas Rahimi and Simone Benatti and Alisha Menon and Senam Tamakloe and Jonathan Ting and Natasha Yamamoto and Yasser Khan and Fred Burghardt and Luca Benini and Ana C. Arias and Jan M. Rabaey},
journal= {arXiv preprint arXiv:1802.10237},
year = {2018}
}