Early detection of physical fatigue in industry using wearable sensors and contextual modeling

Physical fatigue in repetitive production lines contributes to musculoskeletal disorders and absenteeism. This study investigates a pharmaceutical packaging environment in Colombia with 43 operators (42 female; 19–53 years) performing repetitive inspection and packing. Smartwatches captured pulse rate, electrodermal activity, skin temperature, and motion, complemented by demographic (age, experience) and occupational factors (task load, line, shift, timing). Principal Component Analysis (PCA) reduced dimensionality, and a fuzzy logic–based labeling method—adapted from prior controlled experiments—generated binary and four-class fatigue labels without mid-shift self-reports. These labeled datasets were used to train multiple machine-learning classifiers. Integrating contextual features with biometrics substantially improved performance: in binary classification, F1 increased from 0.8848 (biometrics only) to 0.9375; in four-level classification, F1 rose from 0.8232 to 0.8793. Motion-related metrics emerged as the most informative predictors. Critically, feature integration improved reliability: accuracy for intermediate states (Higher Non-Fatigue and Higher Fatigue) rose by ∼10 percentage points, while false negatives in the Pure Fatigue class were eliminated—3% of cases previously misclassified as Higher Non-Fatigue were instead correctly mapped within the fatigue spectrum. This shift strengthens the system’s effectiveness for real-time safety interventions. The novelty of this work lies in combining biometric and contextual modeling to reduce false negatives in critical fatigue states, providing a scalable, non-intrusive, and human-centered early-warning system. By aligning with Industry 5.0, this approach demonstrates how wearable and contextual data can jointly support proactive and trustworthy safety interventions while maintaining operational flow.