Automatic Detection and Recognition of Human Movement Patterns in Manipulation Tasks

Understanding human behavior is an active research area which plays an important role in robotic learning and human-computer interaction. The identification and recognition of behaviors is important in learning from demonstration scenarios to determine behavior sequences that should be learned by the system. Furthermore, behaviors need to be identified which are already available to the system and therefore do not need to be learned. Beside this, the determination of the current state of a human is needed in interaction tasks in order that a system can react to the human in an appropriate way. In this paper, characteristic movement patterns in human manipulation behavior are identified by decomposing the movement into its elementary building blocks using a fully automatic segmentation algorithm. Afterwards, the identified movement segments are assigned to known behaviors using k-Nearest Neighbor classification. The proposed approach is applied to pick-and-place and ball-throwing movements recorded by using a motion tracking system. It is shown that the proposed classification method outperforms the widely used Hidden Markov Model-based approaches in case of a small number of labeled training examples which considerably minimizes manual efforts.