An Image-based Fall Detection System for the Elderly using YOLOv5

A system’s capacity to identify whether a person has fallen is known as fall detection. People who fall have difficulty moving their muscles. Even without injuries, the elderly people are too exhausted to stand up. Currently, there is an increasing amount of focus on the protection of old people living alone. The fall detection system using wearable gadgets and environmental monitoring devices has been introduced. Yet, there are certain problems, such as high invasion, poor accuracy, and weak resilience, due to the usage of the Internet of Things and early fall detection warnings. Elderly people frequently need assistance getting up after falling, and if that assistance is delayed, the consequences of falling could be highly serious. Despite extensive studies on the subject conducted over the past few years, creating non-invasive technologies for automatic fall detection that are trustworthy and acceptable to the people being monitored remains difficult. The geriatric population has continued to increase as a result of medical technological advancements. Because falls can result in fractures and have catastrophic repercussions have received a lot of attention. Therefore, it is crucial for both older individuals and those who care for them to identify fall accidents quickly. Some of the fall detection scenarios are people who are irresponsible and stumble, those who are afflicted with ailments like heart attacks or strokes, children playing on the ground, and senior citizens.