Wrist-worn gesture sensing with wearable intelligence

Liang, X., Ghannam, R. and Heidari, H. (2019) Wrist-worn gesture sensing with wearable intelligence. IEEE Sensors Journal, 19(3), pp. 1082-1090. (doi: 10.1109/JSEN.2018.2880194)

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This paper presents an innovative wrist-worn device with machine learning capabilities and a wearable pressure sensor array. The device is used for monitoring different hand gestures by tracking tendon movements around the wrist. Thus, an array of PDMS-encapsulated capacitive pressure sensors is attached to the user to capture wrist movement. The sensors are embedded on a flexible substrate and their readout requires a reliable approach for measuring small changes in capacitance. This challenge was addressed by measuring the capacitance via the switched capacitor method. The values were processed using a programme on LabVIEW to visually reconstruct the gestures on a computer. Additionally, to overcome limitations of tendon’s uncertainty when the wristband is re-worn, or the user is changed, a calibration step based on the Support Vector Machine (SVM) learning technique is implemented. Sequential Minimal Optimization (SMO) algorithm is also applied in the system to generate SVM classifiers efficiently in real-time. The working principle and the performance of the SVM algorithms demonstrate through experiments. Three discriminated gestures have been clearly separated by SVM hyperplane and correctly classified with high accuracy (>90%) during real-time gesture recognition.

Item Type:Articles
Additional Information:Also supported by EP/R511705/1 from EPSRC, UK, and the Glasgow Exchange Knowledge (GKE) Fund 2017/2018.
Glasgow Author(s) Enlighten ID:Ghannam, Professor Rami and Heidari, Professor Hadi and Liang, Xiangpeng
Authors: Liang, X., Ghannam, R., and Heidari, H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:IEEE Sensors Journal
ISSN (Online):1558-1748
Published Online:12 November 2018
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in IEEE Sensors Journal 19(3):1082-1090
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3028770Magnetic Diagnostics Setup for Lab-on-Chip ApplicationHadi HeidariThe Royal Society (ROYSOC)RSG\R1|180269ENG - Electronics & Nanoscale Engineering
3032700FET-Open Challenging Current Thinking: Magnetic-Assisted Neuromorphic Computing SystemHadi HeidariScottish Funding Council (SFC)PEER1718/03ENG - Electronics & Nanoscale Engineering
3048960EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and CancerAndrew SutherlandEngineering and Physical Sciences Research Council (EPSRC)EP/R511705/1Chemistry