Recognition and classification of human motion based on hidden Markov model for motion database

In some countries, many problems according to aging are pointed out. Decrease of worker's physical ability is one of them. The old workers have high techniques, but physical ability is lower than that of young workers. And it becomes difficult to keep high quality. Hence it is thought that a power assist by robot is needed. The method that increases human motion simply is mainstream conventional power assist method. However, to assist accurately it is thought that robot has to recognize human motion and has to assist fitly. Hence, the system that save and reproduce human motion “motion database” is necessary. Here, to assist accurately, the motion which includes force information is saved to database. In this research, the trajectory information and the force information of human motion is extracted by using bilateral control and it is modeled. To reproduce appropriate motion from database, a search system is needed. For adapting power assist, the search system should be real-time and be able to search at all times. Therefore, in this research, a real-time motion searching method is proposed. The searching method is based on hidden Markov model because human motion has Markov property. Proposed method can search human motion on real-time while human does motion. The viability of proposed method is confirmed by motion search experiment.