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Sequential Unsupervised Motion Segmentation for Skill Evaluation and Comparison

In recent years, the evaluation of motion skills in fields such as dance and exercise has benefited from the proliferation of motion-capture technologies. This study proposes a computationally efficient method for segmenting and comparing time-series human motion data across individuals with varying...

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Principais autores: Ken Kajiwara, Issei Saito, Tomoaki Nakamura, Daichi Mochihashi, Koki Mimura
Format: Artigo
Sprog:Inglês
Udgivet: IEEE 2025-01-01
Serier:IEEE Access
Fag:
Computational efficient motion analysis
Gaussian process
random Fourier features
time-series modeling
unsupervised segmentation
sequential learning
Online adgang:https://ieeexplore.ieee.org/document/11301700/
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