Wearable Resistive-based Gesture-Sensing Interface Bracelet

Chen, Y., Liang, X., Assaad, M. and Heidari, H. (2019) Wearable Resistive-based Gesture-Sensing Interface Bracelet. In: 4th International Conference on UK - China Emerging Technologies (UCET 2019), Glasgow, UK, 21-22 Aug 2019, ISBN 9781728127972 (doi: 10.1109/UCET.2019.8881832)

190800.pdf - Accepted Version



This paper presents a gesture recognition system based on the pressure changes produced by wrist tendon movements for wearable devices. The data of the pressure variations are captured by means of flexible and ultrathin force resistive sensors. A learning algorithm, Support Vector Machine, helps the system to distinguish various hand gestures through developed programming on MATLAB after extracting the key features of data. In order to achieve rapid gesture recognition with a shorter computational time, higher precision and less space complexity, genetic optimization algorithm is used to find the optimal parameter c (cost factor) and g (kernel function parameters) in SVM algorithm. The SVM parameter optimization improves the classification accuracy and the performance of the classifier. Finally, developed wearable resistive-based wrist-worn gesture sensing system classifies the hand gesture with high accuracy (>70 % ) and the results are displayed on the GUIDE user interface.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Heidari, Professor Hadi and Liang, Xiangpeng
Authors: Chen, Y., Liang, X., Assaad, M., and Heidari, H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Published Online:24 October 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in 2019 UK/ China Emerging Technologies (UCET)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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