Dynamic Gesture Recognition Method Based on Self-Learning Sparse Representation

Using wireless body area networks to monitor and recognize human motion is one of the research hotspots in wireless body area networks, and gesture is an important component of human motion. Gesture, as a natural, intuitive, and easy to learn human-computer interaction method, can provide operational convenience for health monitoring in wireless body area networks. Gestures are divided into static and dynamic gestures. Static gestures rely solely on the external shape and contour information of the hand to convey information. Dynamic gestures also include gesture timing information for gesture movements, which can express richer and more accurate information. Data acquisition methods for gesture movements include data glove based, vision based, EMG based, and inertial sensor based methods. This paper studies a dynamic gesture recognition method based on acceleration sensors.