Abstract:With the development of smart mobile devices, such as mobile phones, gesture recognition based on MEMS inertial sensor and embedded system has become a research hotspot. A simple but effective gesture recognition method is proposed here. In gesture defined phase, 14 gestures are defined, and divided into three categories based on the similarity linguistic and operating. In gesture segmentation phase, the gesture was captured with the kinematic features share by all gestures. In gesture recognized phase, the captured gestures are pre-classified by a two-stage decision tree classifier with the acceleration and angular velocity kinematic features of the various categories of gestures respectively. Experiment among 20 experimenters achieved an average accuracy of 96%. Gesture recognition time is less than 0.01S. The results show that the proposed method can recognize gestures rapidly and accurately under embedded system and it meet the requirements of real-time human-computer interaction.