In this study, we developed a low-cost simulated testbed of a physically interactive virtual reality (VR) system and evaluated its efficacy as an occupational virtual trainer for human-robot collaborative (HRC) tasks. The VR system could be implemented in industrial training applications for sensorimotor skill acquisitions and identifying potential task-, robot-, and human-induced hazards in the industrial environments. One of the challenges in designing and implementing such simulation testbed is the effective integration of virtual and real objects and environment, including human movement biomechanics. Therefore, this study aimed to compare the movement kinematics (joint angles) and kinetics (center of pressure) of the human participants while performing pick-and-place lifting tasks with and without using a physically interactive VR testbed. Results showed marginal differences in human movement kinematics and kinetics between real and virtual environment tasks, suggesting the effective transfer of training benefits from VR to real-life situations.
In this study, we developed and validated a full-body musculoskeletal model in OpenSim to estimate muscle and joint forces while performing various motor tasks using a virtual reality (VR) system. We compared the results from our developed full-body musculoskeletal model to those from previous studies by simulating kinematic and kinetic data of participants performing pick-and-place lifting tasks using with and without a physically interactive VR system. Results showed that scaling errors between the two environments are comparable, while the overall errors were consistent with previous studies. Overall, the results from the inverse dynamic simulations showed the promise of our developed OpenSim models in determining potential intervention or prevention strategies to reduce the musculoskeletal injury incidences while simulating human-device interaction tasks.
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