Efficient Unsupervised Temporal Segmentation of Motion Data
This addresses the need for efficient motion analysis in fields like robotics or healthcare, but it appears incremental as it builds on existing unsupervised segmentation techniques.
The paper tackles the problem of automated temporal segmentation of human motion data into actions and primitives using a self-similarity-based method, achieving results in a completely unsupervised manner across multiple sensor modalities.
We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighbourhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.