HME-KG: A method of constructing the human motion encoding knowledge graph based on a hierarchical motion model

Qi Liu; Tianyu Huang; Xiangchen Li

doi:10.1142/S1793962324500326

HME-KG: A method of constructing the human motion encoding knowledge graph based on a hierarchical motion model

Qi Liu, Tianyu Huang^*, Xiangchen Li

^*此作品的通讯作者

计算机学院

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG.

源语言	英语
文章编号	2450032
期刊	International Journal of Modeling, Simulation, and Scientific Computing
卷	15
期	3
DOI	https://doi.org/10.1142/S1793962324500326
出版状态	已出版 - 1 6月 2024

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abstract = "The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG.",

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AU - Li, Xiangchen

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AB - The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG.

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HME-KG: A method of constructing the human motion encoding knowledge graph based on a hierarchical motion model

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