Multilayer Topological Clustering for Human Motion Segmentation

This paper presents a method for human motion segmentation aimed at motion analysis in healthcare and rehabilitation. Motion segmentation involves extracting small movements, known as motion primitives, from a sequence of behavioral patterns. While previous works have utilized unsupervised clustering methods as effective approaches for motion segmentation, many of these methods require prior knowledge to enhance performance. To overcome these challenges, we propose a hierarchical topological clustering method capable of representing spatiotemporal features using GNG and the Pulse Neuron Model. Additionally, we present experiments and discussions to validate the applicability of the proposed method for motion analysis in exercise.