Smart Wristband for Gesture Recognition

Xia, Y., Heidari, H. and Ghannam, R. (2020) Smart Wristband for Gesture Recognition. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205426)

Abstract

This paper aim to design a smart wristband for gesture recognition. Tendon movements around the wrist were measured by FSR sensors as input variables to classify different gestures. Polydimethylsiloxane material (PDMS) was applied to encapsulate FSR sensors, so that the wristband is flexible and suitable for people with different wrist sizes. Subsequently, the sensor data was transmitted to the computer via Bluetooth low energy (BLE) technology. MATLAB was used to train a classifier with ensemble subspace discrimination algorithm. After that, the received signal was processed by this trained classifier and made prediction. The accuracy is about 99.4%. Additionally, the paper explored how predict accuracy would be impacted when twisting the wrist. The result showed that a gesture in different angles was classified as different gestures. Overall, the wristband is rechargeable, portable and can accurately recognize over 6 gestures.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Xia, Yuanjie and Ghannam, Professor Rami and Heidari, Professor Hadi
• Authors: Xia, Y., Heidari, H., and Ghannam, R.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISBN: 9781728194882
• Copyright Holders: Copyright © 2020 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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