Enhanced human fall detection via lightweight MDS-OpenPose framework

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...

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Veröffentlicht in:The Journal of supercomputing Jg. 81; H. 10; S. 1156
Hauptverfasser: Wang, Di, Nan, Gangyang, Xia, Fang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer Nature B.V 15.07.2025
Schlagworte:
Accuracy
Algorithms
Complexity
Datasets
Deep learning
Design
Efficiency
Fall detection
Hazard mitigation
Neural networks
Real time
Wearable computers
ISSN:1573-0484, 0920-8542, 1573-0484
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1573-0484
0920-8542
1573-0484
DOI:10.1007/s11227-025-07628-6