Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors
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 stand...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/22/9291 |
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| author | Sophia Otálora Marcelo E. V. Segatto Maxwell E. Monteiro Marcela Múnera Camilo A. R. Díaz Carlos A. Cifuentes |
| author_facet | Sophia Otálora Marcelo E. V. Segatto Maxwell E. Monteiro Marcela Múnera Camilo A. R. Díaz Carlos A. Cifuentes |
| author_sort | Sophia Otálora |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:27:37Z |
| format | Article |
| id | doaj.art-985c9bbbbf714689a380489382e799b3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:27:37Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-985c9bbbbf714689a380489382e799b32023-11-24T15:06:04ZengMDPI AGSensors1424-82202023-11-012322929110.3390/s23229291Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable SensorsSophia Otálora0Marcelo E. V. Segatto1Maxwell E. Monteiro2Marcela Múnera3Camilo A. R. Díaz4Carlos A. Cifuentes5Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, BrazilTelecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, BrazilFederal Institute of Espírito Santo (IFES), Serra 29040-780, BrazilBristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UKTelecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, BrazilBristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UKMuscle 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.https://www.mdpi.com/1424-8220/23/22/9291muscle fatigueelectromyographyinertial sensorsOptical Fiber Sensorsmachine learning |
| spellingShingle | Sophia Otálora Marcelo E. V. Segatto Maxwell E. Monteiro Marcela Múnera Camilo A. R. Díaz Carlos A. Cifuentes Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors Sensors muscle fatigue electromyography inertial sensors Optical Fiber Sensors machine learning |
| title | Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors |
| title_full | Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors |
| title_fullStr | Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors |
| title_full_unstemmed | Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors |
| title_short | Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors |
| title_sort | data driven approach for upper limb fatigue estimation based on wearable sensors |
| topic | muscle fatigue electromyography inertial sensors Optical Fiber Sensors machine learning |
| url | https://www.mdpi.com/1424-8220/23/22/9291 |
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