Real-time detection and machine learning classification of physical fatigue in construction workers using multi-modal digital biomarkers

Abstract

This study introduces a multimodal wearable sensing framework that combines physiological (heart rate, breathing rate), thermoregulatory (skin temperature, electrodermal activity), and biomechanical (plantar pressure and acceleration) digital biomarkers for objective, real-time fatigue detection in construction workers. Fifteen healthy construction workers, aged 18 years or older, executed a standardized one-hour bar-bending tasks while utilizing wearable devices. Subjective fatigue levels were assessed using the Borg-20. Five supervised machine learning classifiers were assessed utilizing 10-fold cross-validation. In single-biomarker models, thermoregulatory features (Classification Accuracy [CA], 88.9%) surpassed physiological (CA, 72.3%) and biomechanical features (CA, 71.1%). The integration of physiological, thermoregulatory, and biomechanical parameters markedly enhanced classification accuracy (CA, 92.3%). The random forest is the most effective machine learning classifier for both single and multimodal biomarker data. This study constitutes the first direct comparison and integration of these three biomarkers specifically among construction workers, illustrating the evident superiority of multimodal methodologies. The suggested human-centred, AI-driven architecture effectively integrates medical physiology, ergonomics engineering, and wearable technology, providing a scalable, real-time early-warning system for fatigue-related hazards.

Keywords: Physical Fatigue, Construction Workers, Digital Biomarkers, Wearable Sensors, Machine Learning, Human Factors, Occupational Safety, Real-time Monitoring

DOI: 10.54941/ahfe1007358