Analysis and Validation for Kinematic and Physiological Data of VR Training System

Abstract

Virtual reality applications can provide a more immersive environment that improves users’ enthusiasm to participate. For VR-based limb motor training applications, the widespread use of VR techniques still has many challenges. On the one hand, it is not easy to evaluate the effectiveness and accuracy of VR-based programs. On the other hand, monitoring the users’ physical and mental burden during the training process is an essential but difficult task. To this end, we propose a simple and economical VR-based application for limb motor training. Kinematic data are used to monitor the user’s movements quantitatively. We also collect physiological data, including heart rate variability (HRV) and electroencephalogram (EEG) data. HRV data are used to assess physical fatigue in real-time and EEG data can be used to detect mental fatigue in the future. Based on this application, we have conducted many experiments and user studies to verify the kinematic data monitoring accuracy and the feasibility of fatigue detecting. The results have demonstrated that VR-based solutions for limb motor training have good kinematic data measurement precision. Meanwhile, the physiological data demonstrated that the VR-based rehabilitation does not cause too much physical fatigue to participants.