Research Of Gesture Recognition Based On CAS-GLOVE

Recently, with the rapid development of the technology of computer, the interaction between human and computer is more and more continual, and it becomes an important part of our daily life. By the traditional way of interaction, the consumers input the information with the board or the mouse. The abuse of it is that the consumers cannot communicate with the computer with their habitual modes, such as gesture, voice etc. But the modern modes of the interaction break the choke point of the interaction between human and computer adequately represent the idea of"human beings are principal". It is an interaction technology of multimedia and multimode to actualize the interaction between human and computer by gesture or voice.Gesture is a natural, intuitionistic and easy mode for the interaction between human and computer. Compared with the mouse, gesture not only supplies more plentiful space information, but also accord with our habit of the interaction which is much more spontaneous and convenient. Gesture recognition means that we can recognize the meaning according the consumer's gesture. The paper describes a gesture recognition system based on data glove.We use the CAS-Glove developed by CAS. We transform the original data into the angle value according the characteristic of the sensors, and then the precision of the net-training can be effectively improved. This paper analyses the geometric relation of hand shapes. The model of virtual hand is constructed. The tortile angle data of each joint is got from the serial data port of the glove. The standard sample copy library is built. We achieve the gesture recognition with BP Neuron Networks. The network is trained by the standard samples, and it has the function of gesture recognition.We also put forward the method of gesture recognition based on Decision Tree, and achieve real time recognition. This arithmetic is simpler, costs less time, and has a higher recognition rate. The disadvantage is that it causes the accumulation of error easily, so the recognition rate of the sample which is far from the root node is lower, moreover, it can't reject the recognition.