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A Novel Human Abnormal Posture Detection Method Based on Spatial-Topological Feature Fusion of Skeleton

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 15520))

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Abstract

Skeletons can clearly represent human postures, while the skeleton-based human abnormal posture detection has been widely used. Previous skeleton-based methods of human abnormal posture detection are often considered from a single perspective such as joint positions, joint distances and bone angles, without fully utilizing the spatial and topological information of skeleton. To overcome these shortcomings, we propose a novel human abnormal posture detection method based on Spatial-Topological Feature Fusion (STFF) of skeleton. In this study, we present a new definition of spatial similarity of two skeletons called ‘Skeleton Keypoints Displacement Metric’, with the minimum total displacement of all skeleton keypoints. Based on the similarity, we introduce an optimal skeleton matching method to select the optimal matching skeleton from a given set of template skeletons which includes skeletons of typical normal human postures. Then the deviation between the target and the optimal matching skeleton can be regarded as spatial feature to be integrated with the topological feature which can be obtained from the topological structure of the target. At last, we employ a classification method, to achieve human abnormal posture detection. Experiments show that our method achieves state-of-the-art performance on our dataset and FallDown detection dataset.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of Shandong Province (No.ZR2021MF124). Yuefeng Ma is the corresponding author.

Author information

Authors and Affiliations

  1. Qufu Normal University, Rizhao, 276800, China

    Yuefeng Ma, Zhiqi Cheng & Deheng Liu

  2. Qufu Normal University, Jining, 273100, China

    Shiying Tang

Authors
  1. Yuefeng Ma

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  2. Zhiqi Cheng

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  3. Deheng Liu

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  4. Shiying Tang

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Corresponding author

Correspondence toYuefeng Ma.

Editor information

Editors and Affiliations

  1. Nagoya University, Nagoya, Japan

    Ichiro Ide

  2. Centre of Research & Technology, Thermi, Greece

    Ioannis Kompatsiaris

  3. Chinese Academy of Sciences, Beijing, China

    Changsheng Xu

  4. The University of Electro-Communications, Tokyo, Japan

    Keiji Yanai

  5. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  6. Mukogawa Women's University, Nishinomiya, Japan

    Naoko Nitta

  7. Simula, Oslo, Norway

    Michael Riegler

  8. The University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

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© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ma, Y., Cheng, Z., Liu, D., Tang, S. (2025). A Novel Human Abnormal Posture Detection Method Based on Spatial-Topological Feature Fusion of Skeleton. In: Ide, I.,et al. MultiMedia Modeling. MMM 2025. Lecture Notes in Computer Science, vol 15520. Springer, Singapore. https://doi.org/10.1007/978-981-96-2054-8_4

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