Early Detection of Physical Fatigue in Industry via Wearable Biometric Sensors and Contextual Data Modeling

Physical fatigue in repetitive production lines contributes to musculoskeletal disorders and absenteeism. This study investigates a pharmaceutical packaging environment where workers frequently perform repetitive tasks. Biometric data—pulse rate, internal temperature, electrodermal activity, and movement—were collected using smartwatches, alongside demographic (age, experience) and occupational factors (task load, line, shift, timing) Principal Component Analysis was applied to reduce dimensionality and extract key features. A fuzzy logic-based labeling method, adapted from prior controlled studies, was used to assign fatigue levels (binary and four-class) in a non-intrusive and objective way. These labeled datasets trained various machine learning models to classify physical fatigue states. The results demonstrate that incorporating demographic and occupational context (external features) alongside biometric data significantly enhances classification performance. In the binary classification task, the F1 score increased from 0.8848 (biometric data only) to 0.9375 with the addition of external features. Similarly, in the four-level classification, the F1 score improved from 0.8200 to 0.8793 when external features were combined with biometric inputs. Feature importance analysis identified motion-related metrics as the most influential predictors, while confusion matrix results showed a clear reduction in false negatives for critical fatigue states. This work introduces a scalable, human-centered system for early physical fatigue detection in industrial settings, supporting safety, performance, and well-being in line with Industry 5.0 principles.