Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSII.2020.3010318
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dc.titleA Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge
dc.contributor.authorLUO YUXUAN
dc.contributor.authorLI YIDA
dc.contributor.authorTHAM CHEN KHONG
dc.contributor.authorHENG CHUN HUAT
dc.contributor.authorTHEAN VOON YEW, AARON
dc.date.accessioned2021-06-14T02:50:25Z
dc.date.available2021-06-14T02:50:25Z
dc.date.issued2020-07-20
dc.identifier.citationLUO YUXUAN, LI YIDA, THAM CHEN KHONG, HENG CHUN HUAT, THEAN VOON YEW, AARON (2020-07-20). A Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge. IEEE Transactions on Circuits and Systems II: Express Briefs 67 (9) : 1624-1628. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSII.2020.3010318
dc.identifier.issn15583791
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/192015
dc.description.abstractThis brief presents a wireless smart glove based on multi-channel capacitive pressure sensors that is able to recognize 10 American Sign Language gestures at the edge. In this system, 16 capacitive sensors are fabricated on a glove to capture the hand gestures. The sensor data is captured by a 16-channel CDMA-like capacitance-to-digital converter for training/inference at the edge device. Unlike the conventional approach where the capacitive information is recovered before further signal processing, our proposed system approach takes advantage of the capability of the machine learning (ML) algorithms and directly processes the code-modulated signals without demodulation. As a result, it reduces the input data throughput fed into the ML algorithms by 20×. The on-site ML implementation significantly reduces decision-making latency and lowers the required data throughput for wireless transmission by at least 4×. The highest testing classification accuracy of our system achieved is 99.7%, with a <; 0.1% difference from the conventional demodulated sensing scheme.
dc.description.urihttps://ieeexplore.ieee.org/document/9144270
dc.language.isoen
dc.publisherIEEE
dc.rightsCC0 1.0 Universal
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectAI at the edge, capacitive sensors, edge computing, machine learning, multi-channel sensing, smart glove, wireless sensors
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1109/TCSII.2020.3010318
dc.description.sourcetitleIEEE Transactions on Circuits and Systems II: Express Briefs
dc.description.volume67
dc.description.issue9
dc.description.page1624-1628
dc.published.statePublished
dc.grant.idNRF-RSS2015-003
dc.grant.idA18A1b0045
dc.grant.fundingagencyNRF
dc.grant.fundingagencyAME
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