An automatic and non-invasive physical fatigue assessment method for construction workers

Highlights

• Computer vision and biomechanics were merged to assess construction workers’ physical fatigue.
• The method considers muscle capacity, postures and working history were considered with no limitations on working pattern.
• Accuracy and feasibility were tested through lab/field experiments and case studies.
• The method could automatically and continuously provide accurate physical fatigue assessments.
• The method could help in site layout or work schedule for improving safety and health.

Abstract

The construction industry around the globe has unsatisfactory occupational health and safety records. One of the major reasons is attributed to high physical demands and hostile working environments. Construction work always requires workers to work for a long duration without sufficient breaks to recover from overexertion and to work under harsh climatic conditions and/or in confined workspaces. Such circumstances can increase the risk of physical fatigue. Traditionally, fatigue monitoring in the construction domain relies on self-reporting or subjective questionnaires. These methods require the manual collection of responses and are impractical for continuous fatigue monitoring. Some researchers have used on-body sensors for fatigue monitoring (such as heart rate monitors and surface electromyography (sEMG) sensors). Although these devices appear to be promising, they are intrusive, requiring sensors to be attached to the worker’s body. Such on-body sensors are uncomfortable to wear and could easily cause irritation. Considering the limitations of these methodologies, the current research proposes a novel non-intrusive method to monitor the whole-body physical fatigue with computer vision for construction workers. A computer vision-based 3D motion capture algorithm was developed to model the motion of various body parts using an RGB camera. A fatigue assessment model was developed using the 3D model data from the developed motion capture algorithm and biomechanical analysis. The experiment showed that the proposed physical fatigue assessment method could provide joint-level physical fatigue assessments automatically. Then, a series of experiments demonstrated the potential of the method in assessing the physical fatigue level of different construction task conditions such as site layout and the work-rest schedules.

Keywords

Occupational safety and health; Construction worker; Ergonomic; Deep learning; Machine learning; Computer vision