Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication...

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Published in:IEEE reviews in biomedical engineering Vol. 15; pp. 85 - 102
Main Authors: Jiang, Shuo, Kang, Peiqi, Song, Xinyu, Lo, Benny, Shull, Peter
Format: Journal Article
Language:English
Published: United States IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Deep learning
Discriminant analysis
electromyography
Exoskeleton
Exoskeletons
forcemyography
Gesture recognition
Gestures
Hand
Hand (anatomy)
human-computer interaction
Human-computer interface
Humans
Interfaces
Learning algorithms
Machine learning
Machine learning algorithms
Mathematical analysis
Neurological diseases
Prostheses
Prosthetics
Quality of Life
Recovery of function
Rehabilitation
Reproducibility of Results
Stroke (medical condition)
Support vector machines
Training
Wearable Electronic Devices
Wearable technology
Wrist
ISSN:1937-3333, 1941-1189, 1941-1189
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Abstract Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
AbstractList Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.
Author Kang, Peiqi
Shull, Peter
Jiang, Shuo
Song, Xinyu
Lo, Benny
Author_xml – sequence: 1
  givenname: Shuo
  orcidid: 0000-0003-3645-6301
  surname: Jiang
  fullname: Jiang, Shuo
  email: jiangshuo@tongji.edu.cn
  organization: College of Electronics and Information Engineering, Tongji University, Shanghai, China
– sequence: 2
  givenname: Peiqi
  orcidid: 0000-0003-2499-1251
  surname: Kang
  fullname: Kang, Peiqi
  email: pekkykang@sjtu.edu.cn
  organization: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
– sequence: 3
  givenname: Xinyu
  orcidid: 0000-0002-6396-7038
  surname: Song
  fullname: Song, Xinyu
  email: songxinyu@sjtu.edu.cn
  organization: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
– sequence: 4
  givenname: Benny
  orcidid: 0000-0002-5080-108X
  surname: Lo
  fullname: Lo, Benny
  email: benny.lo@imperial.ac.uk
  organization: Department of Surgery and Cancer, Imperial College London, London, U.K
– sequence: 5
  givenname: Peter
  orcidid: 0000-0001-8931-5743
  surname: Shull
  fullname: Shull, Peter
  email: pshull@sjtu.edu.cn
  organization: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33961564$$D View this record in MEDLINE/PubMed
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Snippet Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life....
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SubjectTerms Algorithms
Deep learning
Discriminant analysis
electromyography
Exoskeleton
Exoskeletons
forcemyography
Gesture recognition
Gestures
Hand
Hand (anatomy)
human-computer interaction
Human-computer interface
Humans
Interfaces
Learning algorithms
Machine learning
Machine learning algorithms
Mathematical analysis
Neurological diseases
Prostheses
Prosthetics
Quality of Life
Recovery of function
Rehabilitation
Reproducibility of Results
Stroke (medical condition)
Support vector machines
Training
Wearable Electronic Devices
Wearable technology
Wrist
Title Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey
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Volume 15
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