Comparison of Head Movement Recognition Algorithms in Immersive Virtual Reality Using Educative Mobile Application

Abstract

Virtual reality has been implemented in many fields recently escpecially in education because its capability to produce a virtual world and take users to experience in different level with lower cost. The users will interact with the virtual world using their body or some parts of body such us head, hand, or voice. The problem of recognition accuracy level is still a challenging problem. This research is focused on comparing head movement recognition algorithms in a simple educative mobile application. Accelerometer sensor and RGB camera in Kinect are used to capture five basic head movements: nodding, shaking, looking up, looking down, tilting. Three different algorithms are used to recognize the movement; backpropagation neural network, dynamic time wrapping and convolutional neural network. The result reveals that accelerometer-based dynamic time wrapping method is the best method in recognizing the head movement with 80%, accuracy level, followed by backpropagation neural network and convolutional neural network.