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Comparative Analysis of Human Action Recognition Classification for Ambient Assisted Living

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Advances in Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1864))

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Abstract

Technologies of Ambient Intelligence - an environment filled with computing systems and devices that react and have a certain impact on the presence of a person in this environment. Namely, by making life easier and better for the elderly by designing and developing new functions, services and information and communication technology systems. This study is aimed at reviewing and presenting a new approach to the issue of detecting anomalies and potentially dangerous situations for human health and life - a system for calculating a person's posture by constructing a vector skeleton. The advantage of this method is rather moderate system requirements for computing software and the possibility of its rapid implementation from the planning stage to the final implementation. Extracting the main features from a sequence of images to evaluate parameters such as posture, position and activity of a person and their subsequent categorization and ordering using neural network algorithms. This procedure is necessary to determine the frames for the content of scenarios with a potential threat to an elderly person, for example, a fall. In particular, the most recent and advanced body of libraries has been created in the last five years to make skeleton-based algorithms more accessible for research and subsequent application. Since these libraries will be included for practical applications in video surveillance, medical care. This report presents a comparative classification of various libraries of the human pose calculation system by constructing a vector skeleton based on prepared images and videos. In all tests performed, the parameter for evaluating the performance of these libraries for algorithms such as PoseNet, MoveNet and BlazePose is the percentage of correctly calculated joint vectors. According to the results of the study and the tests carried out, the most effective method for identifying various positions of a person in videos is MoveNet.

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Authors and Affiliations

  1. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ainur Zhumasheva, Madina Mansurova & Gulshat Amirkhanova

  2. International Information Technology University, Almaty, Kazakhstan

    Rollan Alimgazy

Authors
  1. Ainur Zhumasheva
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  2. Madina Mansurova
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  3. Gulshat Amirkhanova
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  4. Rollan Alimgazy
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Corresponding author

Correspondence to Ainur Zhumasheva .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Zhumasheva, A., Mansurova, M., Amirkhanova, G., Alimgazy, R. (2023). Comparative Analysis of Human Action Recognition Classification for Ambient Assisted Living. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_57

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  • DOI: https://doi.org/10.1007/978-3-031-41774-0_57

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-41774-0

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