1. Body Motion Segmentation via Multilayer Graph Processing for Wearable Sensor Signals
- Author
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Qinwen Deng, Songyang Zhang, and Zhi Ding
- Subjects
Motion segmentation ,multilayer graph signal processing ,unsupervised learning ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Human body motion segmentation plays a major role in many applications, ranging from computer vision to robotics. Among a variety of algorithms, graph-based approaches have demonstrated exciting potential in motion analysis owing to their power to capture the underlying correlations among joints. However, most existing works focus on simpler single-layer geometric structures, whereas multi-layer spatial-temporal graph structure can provide more informative results. To provide an interpretable analysis on multilayer spatial-temporal structures, we revisit the emerging field of multilayer graph signal processing (M-GSP), and propose novel approaches based on M-GSP to human motion segmentation. Specifically, we model the spatial-temporal relationships via multilayer graphs (MLG) and introduce M-GSP spectrum analysis for feature extraction. We present two different M-GSP based algorithms for unsupervised segmentation in the MLG spectrum and vertex domains, respectively. Our experimental results demonstrate the robustness and effectiveness of our proposed methods.
- Published
- 2024
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