Real-Time Human Activity Recognition for VR Simulators with Body Area Networks

Abstract

Due to the limited physical space and training facilities, we propose one efficient immersive training method to integrate a virtual reality (VR) simulation system with a body area network (BAN). With the Customized deep neural network algorithm, the body-worn inertial sensors are capable to recognize the activities of participants and avoid mismatched actions. Moreover, the neural networks have been utilized to provide greater access to physical actions of the VR real-time training environment. In this paper, a quaternion based deep neural network algorithm is developed and implemented for human activity recognition (HAR). We share the experience on the VR application that has the potential to fulfil multi-user immersive VR system on HAR.