A machine learning approach for detecting fatigue during repetitive physical tasks

Abstract

Prolonged and repetitive stress on muscles, tendons, ligaments, and nerves can have long-term adverse effects on the human body. This can be exasperated while working if the environment and nature of the tasks puts significant strain on the body, which may lead to work-related musculoskeletal disorders (WMSDs). Workers with WMSDs can experience generalized pain, loss of muscle strength, and loss of ability to continue working. Most WMSDs injuries are caused by ergonomic risks, such as repetitive physical movements, awkward postures, inadequate recovery time, and muscular stress. Fatigue can be seen as a detector of ergonomic risk, as the accumulation of fatigue can significantly increase the possibility of injury. Thirty participants completed a series of repetitive physical tasks over a six-hour period while wearing sensors to capture data related to heart rate and movement, while external embedded sensors captured ground reaction and hand exertion force. They also provided subjective ratings of fatigue at the start and end of the experiment. Classifiers for fatigue (high vs low) were constructed using three methods: linear discriminant analysis (LDA), k-nearest neighbor (kNN), and polynomial kernel-based SVM (P-SVM) and were validated using a tenfold cross-validation technique that was repeated a hundred times. Results of our supervised machine learning approach demonstrated a maximum accuracy of 94.15% using P-SVM for the binary classification of fatigue.