Gesture recognition approach based on learning sparse representation - Ling XIAO; Ren-fa LI; Fan-zai ZENG; Wei-lan QU (Tongxin xuebao)
An approach of robust accelerometer-based hand gesture recognition based on self-learning sparse representa-tion was proposed. This method operated directly on the original acceleration signals by sparse representation without feature extraction and used the class-specific dictionary learning for sparse modeling to reduce the computing cost and time of recognition. The proposed system can easily add a novel gesture category as well as remove existing ones. Ex-periments on real-world database of 18 hand gestures validate the availability of the proposed algorithm.