A depth camera-based full-body reconstruction method for body pose training in occupational safety

Abstract

Occupational injuries have high incidence rates across various industries. Safety education is a key component to effectively reduce work-related injuries. Posture training for work safety is widely adopted to increase the awareness of unsafe movements at work and to evaluate workers to minimize work-related musculoskeletal stresses. However, existing one-size-fits-all pamphlet-based posture training is facing challenges in its effectiveness. In recent years, the substantial technological development in virtual reality (VR) and augmented reality (AR) has made immersive and personalized education possible. For VR/AR-assisted posture training, full-body reconstruction from multiple point clouds is the key step. In this study, we propose a fast and coarse method to reconstruct the full-body pose of safety instructors using multiple low-cost depth cameras. The reconstructed body images from depth cameras are registered through iterative closet point algorithm. The reconstructed full-body pose can be further rendered in VR/AR environments for next-generation safety education.