A Data-Driven Framework to Model Physical Fatigue in Industrial Environments Using Wearable Technologies

Abstract: Industry 4.0 is the tendency towards automation and data exchange in manufacturing and the process sector. However, many manual material handling and repetitive operations can still cause the operators fatigue or exhaustion. Once the operator experiences physical fatigue, their performance decreases. The consequences may result in reduced production quality and efficiency and increased operational human errors that could give rise to incidents and accidents. Over time, physical fatigue can result in more adverse effects for the operators, such as Chronic Fatigue Syndrome (CFS) and Work-related Musculoskeletal Disorders (WSMD). For this reason, from an occupational health and safety point of view, the operator's physical fatigue must be managed. The increasing availability of wearable devices combined with health information can provide real-time measuring and monitoring of physical fatigue in the operational environment while minimally influencing the primary job. This paper presents a physiological signal-based approach using a non-intrusive wristband for continuous health monitoring to predict physical fatigue in industrial-related tasks. These data are used as input to classification algorithms to detect physical fatigue. Accurate and real-time physical fatigue detection helps to improve operator safety and prevent work accidents. Future work will deploy the model in a real-world environment in the industry.

Keywords: Classification algorithms, human performance modelling, Industry 4.0, physical fatigue, physiological parameters, wearable sensors

DOI: 10.54941/ahfe1003016

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