Deep Learning-Powered Fall Detection and Behavior Monitoring Using Computer Vision

Abstract

Advances in modern medicine have clearly improved the human lifespan significantly. But elderly people have some problems in their life such as declining in movement ability due to getting old. They tend to suffer from reduced physical fitness, muscle weakness, decreased coordination, and balance problems. Such reasons lead to the fall, which sometimes leads to deadly consequences. This is particularly important if people live alone, where falls can come with serious health risks that require urgent assistance. Food and health care are also addressed, providing a description of solution-by-solution applications that tend to vary slightly depending on consumer best practices and risk functions, such as reaching out to the individual via phone to assess their condition prior to triggering emergency contact or medical attention should a fall be detected by a monitoring system. Thus, the birth of a new approach that would create a fall detection system that takes advantage of already available security camera video. This paper presents a system that tends to detect falls using computer vision and deep learning. It is trained to differentiate between activity type and fall type. The system incorporates additional functions including multiangle video analysis and multiperson tracking to improve accuracy and assess the severity of the fall, ensuring that urgent responses are prioritized. Enable Alerts for emergency contacts to alert contacts so they can assist a fallen person as soon as possible. This aims to raise living standards of life with a focus on preventing risks

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