A Method of Hand Gesture Recognition Based on Multiple Sensors

This paper presents a new method of gesture recognition based on multiple sensors fusion technique. Three kinds of sensors, namely surface Electromyography (sEMG) sensor, 3-axis accelerometer (ACC) and camera, are used together to capture the dynamic hand gesture firstly. Then four types of features are extracted from the three kinds of sensory data to depict the static hand posture and dynamic gesture trajectory characteristics of hand gesture. Finally decision-level multi-classifier fusion method is implemented for hand gesture pattern classification. Experimental results of 4 subjects demonstrate that each kind of sensor data has its advantages and disadvantages in representing hand gestures. And the proposed method could fuse effectively the complementary information from these three types of sensors for dynamic hand gesture recognition.