关键词: 手势识别, 表面肌电信号（sEMG）, 加速度信号（ACC）, 陀螺仪信号（GYRO）, 多流隐马尔可夫模型（MHMMs）

Abstract: This paper studies using three kinds of sensors（surface electromyography, gyroscope and accelerometer） singnal characteristics for information fusion. The purpose is to improve the types and accuracy of recognizable dynamic hand gestures. Dynamic hand gestures are divided into three elements: hand shape, gestures towards and trajectory. The three elements are respectively represented by surface Electromyogram signal（sEMG）, Gyroscope signal（GYRO） and Acceleration signal（ACC）. Multi-streaming HMMs are used for pattern recognition of dynamic hand gestures. In the recognition experiments that contain five motion trajectories and six static hand shapes, the results show that this method can effectively identify dynamic hand gestures from the continuous signals. The global average recognition rate obtained from the composite use of three kinds of sensors is higher than 92%. It is significantly higher than the average recognition rate that obtained from the combination of any two sensors or single sensor only. Experimental results show that this method is an effective dynamic gesture recognition method. And compared with the traditional dynamic gesture recognition method, this method has more advantages.

Key words: gesture recognition, surface Electromyogram signal（sEMG）, Acceleration signal（ACC）, Gyroscope signal（GYRO）, Multi-stream Hidden Markov Models（MHMMs）