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

Kyung Rok Pyun; Kangkyu Kwon; Myung Jin Yoo; Kyun Kyu Kim; Dohyeon Gong; Woon-Hong Yeo; Seungyong Han; Seung Hwan Ko

doi:10.1093/nsr/nwad298

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

Natl Sci Rev. 2023 Nov 27;11(2):nwad298. doi: 10.1093/nsr/nwad298. eCollection 2024 Feb.

Authors

Kyung Rok Pyun¹, Kangkyu Kwon^{1

2

3}, Myung Jin Yoo¹, Kyun Kyu Kim⁴, Dohyeon Gong⁵, Woon-Hong Yeo^{2

6}, Seungyong Han⁵, Seung Hwan Ko^{1

7}

Affiliations

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

Abstract

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Keywords: artificial intelligence; gesture recognition; human–machine interfaces; machine learning; soft electronics; wearable sensor.

Publication types

Review