Wearable Wristworn Gesture Recognition Using Echo State Network

Wang, W., Liang, X., Assaad, M. and Heidari, H. (2020) Wearable Wristworn Gesture Recognition Using Echo State Network. In: 26th IEEE International Conference on Electronics Circuits and Systems (ICECS 2019), Genova, Italy, 27-29 Nov 2019, pp. 875-878. ISBN 9781728109961 (doi: 10.1109/ICECS46596.2019.8965219)

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This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Heidari, Dr Hadi and Liang, Xiangpeng
Authors: Wang, W., Liang, X., Assaad, M., and Heidari, H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Copyright Holders:Crown Copyright © 2019
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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