Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

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作者： Kyung Rok,Pyun ^[1] ; Kangkyu,Kwon ^[1] ; Myung Jin,Yoo ^[2] ; Kyun Kyu,Kim ^[3] ; Dohyeon,Gong ^[1] ; Woon-Hong,Yeo ^[4] ; Seungyong,Han ^[5] ; Seung Hwan,Ko ^[2]

作者单位： Department of Mechanical Engineering, Seoul National University, Seoul08826, South Korea. ^[1] IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA30332, USA. ^[2] School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA30332, USA. ^[3] Department of Chemical Engineering, Stanford University, Stanford, CA94305, USA. ^[4] Department of Mechanical Engineering, Ajou University, Suwon-si16499, South Korea. ^[5] George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA30332, USA. ^[6] Institute of Advanced Machinery and Design (SNU-IAMD), Seoul National University, Seoul08826, South Korea. ^[7]

关键词 artificial intelligence gesture recognition human–machine interfaces machine learning soft electronics wearable sensor

DOI 10.1093/nsr/nwad298

PMID 38213520

发布时间 2024-01-13

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National science review

2024年11卷2期

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

National science review

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