Please use this identifier to cite or link to this item: https://doi.org/10.1021/acssensors.1c01698
Title: Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition
Authors: Jieming Pan 
Yida Li 
Yuxuan Luo 
Xiangyu Zhang 
Xinghua Wang 
David Liang Tai Wong 
Chun-Huat Heng 
Chen-Khong Tham 
Aaron Voon-Yew Thean 
Keywords: human−machine-interface hybrid-flexible gestures recognition wearable sensor bimodal sensing capacitive sensing low power sensor array
Issue Date: 2-Nov-2021
Publisher: ACS sensors
Citation: Jieming Pan, Yida Li, Yuxuan Luo, Xiangyu Zhang, Xinghua Wang, David Liang Tai Wong, Chun-Huat Heng, Chen-Khong Tham, Aaron Voon-Yew Thean (2021-11-02). Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition. ACS Sensors 6 (11) : 4156-4166. ScholarBank@NUS Repository. https://doi.org/10.1021/acssensors.1c01698
Rights: Attribution-NoDerivatives 4.0 International
Abstract: As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human–machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybrid-flexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.
Source Title: ACS Sensors
URI: https://scholarbank.nus.edu.sg/handle/10635/218157
ISSN: 2379-3694
DOI: 10.1021/acssensors.1c01698
Rights: Attribution-NoDerivatives 4.0 International
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