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Data-driven approach for upper limb fatigue estimation based on wearable sensors

Otálora, Sophia; Segatto, Marcelo E. V.; Segatto, Marcelo E.V.; Monteiro, Maxwell E.; Múnera, Marcela; Díaz, Camilo A.R.; Cifuentes, Carlos A.

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Authors

Sophia Otálora

Marcelo E. V. Segatto

Marcelo E.V. Segatto

Maxwell E. Monteiro

Marcela Múnera

Camilo A.R. Díaz

Carlos A. Cifuentes



Abstract

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.

Journal Article Type Article
Acceptance Date Nov 14, 2023
Online Publication Date Nov 20, 2023
Publication Date Nov 20, 2023
Deposit Date Jan 19, 2024
Publicly Available Date Jan 24, 2024
Journal Sensors
Electronic ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 23
Issue 22
Article Number 9291
DOI https://doi.org/10.3390/s23229291
Keywords Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Public URL https://uwe-repository.worktribe.com/output/11473589

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