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作 者:Hyeyun Lee Soyoung Lee Jaeseong Kim Heesoo Jung Kyung Jae Yoon Srinivas Gandla Hogun Park Sunkook Kim
机构地区:[1]Department of Advanced Materials Science and Engineering,Sungkyunkwan University,Suwon 16419,Republic of Korea [2]Department of Electrical and Computer Engineering,Sungkyunkwan University,Suwon 16419,Republic of Korea [3]Department of Artificial Intelligence,Sungkyunkwan University,Suwon 16419,Republic of Korea [4]Department of Physical&Rehabilitation Medicine,Kangbuk Samsung Hospital,Sungkyunkwan University School of Medicine,Seoul 03181,Republic of Korea [5]Department of Computer Science and Engineering,Sungkyunkwan University,Suwon 16419,Republic of Korea
出 处:《npj Flexible Electronics》2023年第1期361-373,共13页npj-柔性电子(英文)
基 金:the National Research Foundation of Korea(NRF-2022M3D1A2083618,2021R1A2B5B02002167,2021R1I1A1A01060078,and 2021R1C1C1005407);supported by the SKKU Research Fellowship Program of the Sungkyunkwan University and the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2019-0-00421,Artificial Intelligence Graduate School Program(Sungkyunkwan University));supported by the SafeD Inc.and BECS Inc.
摘 要:With advances in artificial intelligence(AI)-based algorithms,gesture recognition accuracy from sEMG signals has continued to increase.Spatiotemporal multichannel-sEMG signals substantially increase the quantity and reliability of the data for any type of study.Here,we report an array of bipolar stretchable sEMG electrodes with a self-attention-based graph neural network to recognize gestures with high accuracy.The array is designed to spatially cover the skeletal muscles to acquire the regional sampling data of EMG activity from 18 different gestures.The system can differentiate individual static and dynamic gestures with~97%accuracy when training a single trial per gesture.Moreover,a sticky patchwork of holes adhered to an array sensor enables skin-like attributes such as stretchability and water vapor permeability and aids in delivering stable EMG signals.In addition,the recognition accuracy(~95%)remained unchanged even after long-term testing for over 72 h and being reused more than 10 times.
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