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Cited by 10Muscle activation and electromyography studiesContext-Aware Activity Recognition SystemsMechanics and Biomechanics Studies

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

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Putcha, Arjun, Nguyen, Tien, Smith, Regina, Choffin, Rachel, Bai, Wubin·2023·Advanced Intelligent Systems
DOI 10.1002/aisy.202200351
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Abstract

Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In the context of human–machine interfaces, muscle trackers can serve as the “sensory” component, providing real‐time information to machines, such as exoskeletons and prosthetics, that can act upon such information for therapeutic and functional aid. Recently developed devices for muscle tracking rely on combinations of sensor modalities and algorithms to extract information from biosignals track muscle activity, or even extrapolate kinematic information, including gestures. However, a number of obstacles remain to be overcome to further facilitate the practical implementation of muscle‐tracking technologies, the most notable being real‐time analysis of biosignal data and extracting kinematic information from complex movements. This review attempts to cover the mechanisms behind various sensor modalities and algorithms commonly used for muscle tracking, as well as establish the current state of applications within the field. Given its multidisciplinary nature and ability to free users from rehabilitation constraints, the field of muscle tracking holds significant promise for future study.

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Authors
Putcha, A., Nguyen, T., Smith, R., Choffin, R., & Bai, W.
Affiliations
University of North Carolina at Chapel Hill (US)
Corresponding author
Bai, Wubin
ORCID
Venue
Advanced Intelligent Systems
Publisher
Wiley
Published
2023-03-23
Volume
5
Issue
7
ISSN
2640-4567 (ISSN-L)
DOI
10.1002/aisy.202200351
OpenAlex
W4360619235
Cited by
10
References
143
Citations by year
2026: 3 · 2025: 4 · 2024: 3
FWCI
0.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Advanced Intelligent Systems (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of North Carolina at Chapel Hill
  • National Science Foundation (ECCS-2139659)
  • National Institutes of Health (UL1TR002489)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Biomedical Engineering › Mechanics and Biomechanics Studies
Keywords
electromyography, physiological signals, human-machine interaction, exoskeleton, prosthetics, gesture recognition, neuromuscular disorders, rehabilitation

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