Real-time VR Strategy Chess Game using Motion Recognition

Study on Augmented Context Interaction System for Virtual Reality Animation Using Wearable Technology

“…Virtual reality is a technology in which the users can have real-time interaction in a virtual space created by computer systems. It is a convergent technology that allows users to feel immersed in this virtual space through the five senses of the human body and that provides a feeling of presence as if the user actually existed in that space [12]. There are a variety of types of virtual reality content including CAVE type, desktop type, and third person type, but in this study we focus on wearable display HMD-based virtual reality movies, which are the most appropriate for personal media and growing at the fastest speed [13].…”

A Deep Learning Algorithm for Fusing Action Recognition and Psychological Characteristics of Wrestlers

“…This paper adopts a human body identification method based on bone positioning. The first branch takes the overall skeleton point sequence [24,25,12,11,10,9,21,5,6,8,7,8,22,23,4,3,21,2,1,17,18,19,20,13,14,15,16] are input to a two-layer LSTM network, and the second layer of LSTM extracts the entire frame information. The second branch divides the body skeleton point sequence into the left branch [24,25,12,11,10,9,17,18,19,20], the torso [1,2,3,21,4] and the right branch [22,23,8,7,6,5,13,14,15,…”

“…The double-layer LSTM network is input, and the second layer of LSTM accepts the output of the first layer and processes it, making the judgment of actions more accurate. The third branch divides the body skeleton point sequence into the left arm [5,6,7,8,22,23], the right arm [9,10,11,12,24,25], the left leg [13,14,15,16], right leg [17,18,19,20] and torso [1,2,3,21,4], this branch temporal model is similar to the local temporal model to divide the human skeleton into more parts in order to better identify local detailed actions. It can effectively improve the accuracy of detailed action recognition.…”

“…The time length of an action skeleton sequence is t={1,2,3,…,T}, i={1,2,3,…,N}, and the i-th skeleton point at time t is expressed as . Convolutional layer conv1: The size of the convolution kernel is (3,3), and the number of channels is 32. Pooling layer pool1: Using pooling for dimensionality reduction, the pooling kernel is (2,2), and the sliding step stride is 2.…”

“…The 3D coordinates of the joints were mapped to red, green and blue values, thus, action classification became an image classification problem and algorithms from this field could be applied [2]. Kim YK conducted research on human motion recognition based on virtual reality (VR) technology [3]. Wu Q aimed to develop an effective method to identify upper limb movements based on EMG signals for community-based rehabilitation.…”