Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors Article Swipe
Sophia Otálora
,
Marcelo E. V. Segatto
,
Maxwell E. Monteiro
,
Marcela Múnera
,
Camilo A. R. Díaz
,
Carlos A. Cifuentes
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23229291
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23229291
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23229291
- OA Status
- gold
- Cited By
- 10
- References
- 107
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388834202
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388834202Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23229291Digital Object Identifier
- Title
-
Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable SensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-20Full publication date if available
- Authors
-
Sophia Otálora, Marcelo E. V. Segatto, Maxwell E. Monteiro, Marcela Múnera, Camilo A. R. Díaz, Carlos A. CifuentesList of authors in order
- Landing page
-
https://doi.org/10.3390/s23229291Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s23229291Direct OA link when available
- Concepts
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Muscle fatigue, Electromyography, Wearable computer, Inertial measurement unit, Work (physics), Physical medicine and rehabilitation, Wrist, Computer science, Elbow, Artificial intelligence, Simulation, Engineering, Medicine, Radiology, Surgery, Embedded system, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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107Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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