Sensor Reduction on EMG-based Hand Gesture Classification

Giovanni Costantini, Gianni Saggio, Lucia Quitadamo, Daniele Casali, Alberto Leggieri, Emanuele Gruppioni


This work concerns a system based on EMG sensors, signal conditioning circuitry, classification algorithm based on Artificial Neural Network, and virtual avatar representation, useful to identify hand movements within a set of five. This is to potentially make any trans-radial upper-limb amputee able to drive a virtual or real limb prosthetic hand. When using six EMG sensors, the system is able to recognize with an accuracy of 88.8% the gestures performed by a subject, and replicated by an avatar. Here we focused on differences resulting with the adoption of a different number of sensors and therefore, by means of a very simple heuristic method, we compared different subsets of features, excluding the less significant sensors. We found optimal subsets of one, two, three, four and five sensors, demonstrating a decrease of the performance of only 0.8% when using five sensors, while with three sensors the accuracy can be as high as 81.7%.


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Paper Citation

in Harvard Style

Costantini G., Saggio G., Quitadamo L., Casali D., Leggieri A. and Gruppioni E. (2014). Sensor Reduction on EMG-based Hand Gesture Classification . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 138-143. DOI: 10.5220/0005040501380143

in Bibtex Style

author={Giovanni Costantini and Gianni Saggio and Lucia Quitadamo and Daniele Casali and Alberto Leggieri and Emanuele Gruppioni},
title={Sensor Reduction on EMG-based Hand Gesture Classification},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Sensor Reduction on EMG-based Hand Gesture Classification
SN - 978-989-758-054-3
AU - Costantini G.
AU - Saggio G.
AU - Quitadamo L.
AU - Casali D.
AU - Leggieri A.
AU - Gruppioni E.
PY - 2014
SP - 138
EP - 143
DO - 10.5220/0005040501380143