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<p>Falls among the elderly pose a significant risk of injury and even mortality, underscoring the importance of real-time monitoring systems to mitigate these hazards. Existing posture estimation-based fall detection methods often struggle with high parameter counts, computational complexity, and slow processing speeds. This paper proposes an improved OpenPose algorithm, termed MDS-OpenPose, which addresses these issues. By integrating the lightweight MobileNetV3 network to replace the original VGG feature extraction network, optimizing convolutional layer sizes, and introducing DenseNet dense connections, MDS-OpenPose significantly reduces model complexity while maintaining high accuracy. Fall detection is achieved through a comprehensive method that analyzes vertical distances between the head and feet, trunk tilt angles, and horizontal displacement of the center of mass. Experimental results demonstrate that MDS-OpenPose achieves a substantial improvement in FPS on the COCO dataset while maintaining high precision and recall rates. On the Fall Down dataset, it attains an accuracy of 93.0% and a precision of 92.1%. This achievement demonstrates that supercomputing capabilities can effectively improve the real-time performance and reliability of algorithms in actual fall detection scenarios, highlighting the application value of this research in the field of supercomputing.</p>

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Enhanced human fall detection via lightweight MDS-OpenPose framework

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Di Wang,
  • Gangyang Nan,
  • Fang Xia

摘要

Falls among the elderly pose a significant risk of injury and even mortality, underscoring the importance of real-time monitoring systems to mitigate these hazards. Existing posture estimation-based fall detection methods often struggle with high parameter counts, computational complexity, and slow processing speeds. This paper proposes an improved OpenPose algorithm, termed MDS-OpenPose, which addresses these issues. By integrating the lightweight MobileNetV3 network to replace the original VGG feature extraction network, optimizing convolutional layer sizes, and introducing DenseNet dense connections, MDS-OpenPose significantly reduces model complexity while maintaining high accuracy. Fall detection is achieved through a comprehensive method that analyzes vertical distances between the head and feet, trunk tilt angles, and horizontal displacement of the center of mass. Experimental results demonstrate that MDS-OpenPose achieves a substantial improvement in FPS on the COCO dataset while maintaining high precision and recall rates. On the Fall Down dataset, it attains an accuracy of 93.0% and a precision of 92.1%. This achievement demonstrates that supercomputing capabilities can effectively improve the real-time performance and reliability of algorithms in actual fall detection scenarios, highlighting the application value of this research in the field of supercomputing.