Wearable Technology and Machine Learning for Assessing Physical Fatigue in Industry 4.0

Abstract: Industry 4.0 is a shift towards automation and data integration in manufacturing and process sectors. However, manual material handling and repetitive operations still cause significant physical strain on operators, leading to fatigue and exhaustion. This fatigue not only hampers performance but also compromises production quality and efficiency, potentially leading to human errors and accidents. Prolonged exposure to physical fatigue can lead to conditions like chronic fatigue syndrome (CFS) and work-related musculoskeletal disorders (WMSDs). Given these implications, safeguarding occupational health and safety necessitates a proactive approach to managing operator physical fatigue. This study uses wearable devices and health information to propose a real-time measurement and monitoring solution for operator physical fatigue in operational environments. The Empatica EmbracePlus smartwatch was used to quantify fatigue during simulated industrial tasks. Participants engaged in repetitive tasks, while the device monitored vital indicators like heart rate, electrodermal activity, and skin temperature. Self-reported fatigue levels were assessed using the Borg scale to provide ground truth labels for the collected data. The acquired dataset served as input for machine learning models to classify physical fatigue into discrete levels, ranging from 2 to 5 distinct categories. The results highlight the efficacy of the XGBoost algorithm in accurately classifying physical fatigue, demonstrating a classification accuracy of 94.1% for five levels and 99.4% for three levels and the pulse rate as the more reliable indicator of fatigue levels. Additionally, a Bayesian Neural Network model, while producing similar results to the XGBoost algorithm, offers the added advantage of providing credible intervals for its predictions. This research lays the foundation for future deployments of the developed human performance model in real-world industrial environments.

Keywords: Fatigue detection, machine learning, human performance, physiological parameters, Industry 4.0, wearable devices

DOI: 10.54941/ahfe1005052

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