Towards Semantic Segmentation of Human Motion Sequences

In robotics research is an increasing need for knowledge about human motions. However humans tend to perceive motion in terms of discrete motion primitives. Most systems use data-driven motion segmentation to retrieve motion primitives. Besides that the actual intention and context of the motion is not taken into account. In our work we propose a procedure for segmenting motions according to their functional goals, which allows a structuring and modeling of functional motion primitives. The manual procedure is the first step towards an automatic functional motion representation. This procedure is useful for applications such as imitation learning and human motion recognition. We applied the proposed procedure on several motion sequences and built a motion recognition system based on manually segmented motion capture data. We got a motion primitive error rate of 0.9 % for the marker-based recognition. Consequently the proposed procedure yields motion primitives that are suitable for human motion recognition.