Emg Acquisition and Hand Pose Classification for Bionic Hands from Randomly-Placed Sensors
Sumit A. Raurale, John McAllister, Jesús Martínez del Rincón
Abstract
This paper presents a unique real-time motion recognition system for Electromyographic (EMG) signal acquisition and classification. It is the first approach which can classify hand poses from multi-channel EMG signals gathered from randomly placed arm sensors as accurately as current placed-sensor EMG acquisition approaches. It combines time-domain feature extraction, Linear Discriminant Analysis (LDA) feature projection and Multilayer Perceptron (MLP) classification to allow nine distinct poses to be correctly identified more than 95% of the time. This is comparable to state-of-the-art placed-sensor EMG acquisition systems. Processing times of 11.70 ms also make this a viable candidate approach for real-time EMG acquisition and processing in practical prosthesis applications.
BibTeX
@inproceedings{icassp2018_emgacquisitionan,
title = {Emg Acquisition and Hand Pose Classification for Bionic Hands from Randomly-Placed Sensors},
author = {Sumit A. Raurale and John McAllister and Jesús Martínez del Rincón},
booktitle = {ICASSP 2018},
year = {2018}
}