Manipulative Familiarization and Fatigue Evaluation Using Contact State Transition

In this research, we propose a method for generating templates used for manipulation recognition that considers familiarization and fatigue. Our recognition system allows quantitative comparison of similarities among manipulations performed by observing contact state transitions on a palm surface. The system detects the contact states on the palm surface using an attached tactile sensor sheet on a manipulated object. In experiments, familiarization and fatigue are detected by measuring the variance of the manipulation. Experimental results indicate that manipulation can be divided into three periods: learning, familiarization, and fatigue. The results also indicate that variation of the manipulation becomes small when the template is generated by manipulation data during the familiarization period