2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 265-277, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ajay Singh 1 * , Alok Katiyar 2
Doi: https://doi.org/10.54216/FPA.190219
Elderly health has always been a matter of concern for the medical doctors and researchers to come up with advanced recovery techniques. With the rise in population of elderly people and mostly residing alone at home in solitude has motivated many researchers to work on remedial measures for the biggest safety risk faced by them which is elderly fall prevention and mitigating thereby causes of injuries. In this paper, an intelligent deep learning and computer vision based elderly fall recognition system is designed which utilizes advanced spatial-channel decoupled downsampling in You Only Look Once version 10 (YOLOv10), pytorch, darknet and cascaded CNN technologies for the fall detection. The results after testing manifest that the accuracy of the proposed system to recognize and detect the elderly fall is quite assuring, the values of accuracy and mean Average Precision (mAP50) coming out to be 92.46% and 94.1% respectively after the model validation. Moreover, the system displays a real time performance as it can process approximately 45 frames of images per second that realizes a real-time identification of elderly fall patterns. As compared to previous models, the proposed model is much more efficient and has shown promising results.
YOLOv10 Object Detection Algorithm , Computer Vision , Machine Learning , Image Processing , Spatial-channel decoupled down-sampling technique , Non-maximum suppression
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