Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSII.2020.3010318
Title: A Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge
Authors: LUO YUXUAN 
LI YIDA 
THAM CHEN KHONG 
HENG CHUN HUAT 
THEAN VOON YEW, AARON 
Keywords: AI at the edge, capacitive sensors, edge computing, machine learning, multi-channel sensing, smart glove, wireless sensors
Issue Date: 20-Jul-2020
Publisher: IEEE
Citation: LUO 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
Rights: CC0 1.0 Universal
Abstract: This 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.
Source Title: IEEE Transactions on Circuits and Systems II: Express Briefs
URI: https://scholarbank.nus.edu.sg/handle/10635/192015
ISSN: 15583791
DOI: 10.1109/TCSII.2020.3010318
Rights: CC0 1.0 Universal
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ISICAS2020.pdf1.05 MBAdobe PDF

OPEN

Post-printView/Download

SCOPUSTM   
Citations

5
checked on Dec 2, 2021

Page view(s)

72
checked on Dec 2, 2021

Download(s)

2
checked on Dec 2, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons