Development of a Fatigue Estimation Model For Industrial Workers
Nome: SOPHIA OTÁLORA GONZÁLEZ
Data de publicação: 26/03/2024
Banca:
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| CAMILO ARTURO RODRIGUEZ DIAZ | Orientador |
Resumo: Muscle fatigue (MF) reduces the ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders (MSD), which can significantly impact the ability of workers to engage in repetitive tasks over extended periods. MSDs represent a major health concern in physical labor, affecting individuals’ quality of life and the ability to perform daily activities and work-related tasks. Estimating and analyzing MF has broad applications in sports, medicine, and ergonomics.
Specifically, in ergonomics, reducing local muscular workloads is essential for maintaining the health and productivity of workers. Manual lifting, a common practice in various work environments, can contribute to excessive MF, affecting occupational safety, well-being, and overall productivity. During fatigue, kinematic changes occur, altering muscle activity, joint kinematics, and
postural control. Various techniques, both invasive and non-invasive, are used for estimating MF. Invasive methods, such as blood samples or muscle biopsies, provide post-activity information but lack real-time monitoring. Non-invasive methods, like surface electromyography (sEMG), and wearable devices, such as Inertial Measurement Units (IMUs) and Optical Fiber Sensors (OFS), offer alternative approaches to MF estimation. Although EMG remains the gold standard for measuring muscle fatigue, its limitations, such as inaccurate readings in long-term work, motivate the use of alternative wearable devices. This master thesis proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices like OFS and IMUs along the subjective Borg scale. EMG sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. Also, a validation of the OFS sensor before the tests is performed. This study involves 30 subjects performing a repetitive lifting activity until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities 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 Light Gradient Boosting Machine (LGBM) 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.
