A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

Abstract

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.

Publication DOI: https://doi.org/10.3389/fnbot.2019.00114
Divisions: College of Health & Life Sciences > School of Biosciences > Biomedical Engineering
College of Health & Life Sciences > School of Optometry > Optometry & Vision Science Research Group (OVSRG)
College of Health & Life Sciences
Additional Information: © 2020 Esposito, Andreozzi, Gargiulo, Fratini, D’Addio, Naik and Bifulco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Uncontrolled Keywords: Exergaming,Hand gesture recognition,Human–machine interface,Muscle sensors array,Piezoresistive sensor,Support vector machine,Biomedical Engineering,Artificial Intelligence
Full Text Link:
Related URLs: https://www.fro ... 2019.00114/full (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-01-17
Accepted Date: 2019-12-17
Authors: Andreozzi, Emilio
Esposito, Daniele
Gargiulo, Gaetano Dario
Fratini, Antonio (ORCID Profile 0000-0001-8894-461X)
d'Addio, Giovanni
Naik, Ganesh R
Bifulco, Paolo

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