PIs: PI: Xu Xu, Karen Chen, Tianfu Wu, Jing Feng
North Carolina State University
he project will design and research an immersive and personalized education approach by leveraging augmented reality (AR) to support workers’ understanding of risk factors associated with their work, and encourage workers to perform tasks using appropriate body motion. The project will develop an AR-assisted posture training procedure for minimizing biomechanical exposures on the low back and the shoulder, as injuries of those two body regions are the most common in logistics industry. The research will expand knowledge in AR-assisted education and training in terms of work safety promotion, motor adaptation, and transfer of learning. The AR infrastructure can also used to provide just-in-time work procedure training and has potential applications in high school and college courses in physics and biomechanics. The project will advance research in to motor learning, such as sports training and tele-rehabilitation. The overall outcome will advance human-technology system in body motion education and learning science.
The project team will reconstruct a virtual instructor demonstrating the recommended and non-recommended body motions that are commonly seen in material handling. AR, depth sensing, and biomechanical modeling will be integrated to deliver knowledge on work-related biomechanical exposures on joints and muscles. Workers will be able to observe the virtual instructor overlaid on the actual physical workplace from various view angles through AR goggles. The system will be designed to enhance workers’ understanding of work-related risks via visualization of abstract biomechanical exposures. Biomechanical exposures, such as forces exerted on the joints and the muscles at work, will be visually integrated with the virtual instructor. The innovative geometric representation of biomechanical exposures will assist learners in visualizing abstract mechanics quantities. The AR user interface and the biomechanical visualization scheme will be iteratively refined through a series of usability evaluations and operational tests to ensure worker’s understanding of the risk in musculo-skeletal disorder. The research will also examine worker learning and transfer of learning in AR-assisted safety education approach.