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Fall detection based on dynamic key points incorporating preposed attention.

Kun Zheng1, Bin Li1, Yu Li1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Mathematical Biosciences and Engineering : MBE
|June 16, 2023
PubMed
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Accurate fall detection in elderly individuals is crucial. This study introduces a novel deep learning model that fuses human pose and dynamic key point information, significantly improving fall detection accuracy for better elderly care.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Gerontology

Background:

  • Accidental falls are a major health risk for the elderly population.
  • Current video-based deep learning fall detection methods often rely solely on human posture or key points.
  • There is a need for more accurate and robust fall detection systems to mitigate the impact of falls.

Purpose of the Study:

  • To propose a novel fall detection algorithm that combines human pose and dynamic key point information.
  • To enhance the accuracy of fall detection in elderly individuals using surveillance videos.
  • To improve elderly care through more reliable fall detection technology.

Main Methods:

  • Developed a preposed attention capture mechanism for image input into the training network.
Keywords:
complementary correctiondecision fusiondynamic key pointsfall detectionpreposed attention

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  • Introduced the concept of 'dynamic key points' to address incomplete pose information during falls.
  • Fused dynamic key point information with original human posture images and employed an attention expectation mechanism.

    • Main Results:

    • The proposed model effectively fuses human dynamic key point information with posture images.
    • Experiments on the Fall Detection Dataset and UP-Fall Detection Dataset showed improved fall detection accuracy.
    • The dynamic key points model corrects detection errors made by raw human pose image models.

    Conclusions:

    • The proposed fall detection algorithm significantly enhances accuracy by integrating complementary data sources.
    • This approach offers a more robust solution for detecting falls in elderly populations.
    • The developed model provides better support for elderly care applications.