Abstract
Fortuitous collapses are a significant concern for people, particularly who are frail owing to agedness, disability, or further impairments. According to World Health Organization data, significant falls kill over 646,000 people per year, with the majority of these deaths occurring in underdeveloped nations, particularly among persons aged 65 and over. The physiological changes of elderly individuals make them weaker, making them more susceptible to black out and collapsing. Consequences of those falls might be devastating, resulting in long-term hospitalization or, even worse, premature death. The fall’s criticality is determined by its impact. It might be a slight or catastrophic injury, but observers have an obligation to assist the sufferer. What happens, though, if no one is available to assist or if the casualties go unreported by society? This is where a fall recognition architecture comes in handy. The detected falls are designed to identify a fall quickly and send a warning to local aid centers or concerned people so that the individuals might be rescued as soon as possible.
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Arun, D.K., Sumukh Subramanya, H.K., Goel, T., Tanush, N., Nayak, J.S. (2022). Video-Based Elderly Fall Detection Using Convolutional Neural Networks. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_59
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