Building AI-system for fall detection

Abstract

Falls are a serious health concern, particularly among older people and the disabled, because they contribute to increased mortality, hospitalization, and loss of independence. This is especially true for the elderly. For the purpose of solving this issue, we have developed a brand new artificial intelligence system for fall detection. To reliably detect falls, our system employs the powerful YOLOv8 detection algorithm, as well as advanced computer vision algorithms. The YOLOv8 algorithm, which serves as the foundation for our deep learning model, helps us able to detect if an individual person captured by surveillance cameras is falling in acceptable counting time at the staging level. In our opinion, this model successfully differentiates falls from other movements and activities, exhibiting excellent accuracy and a low false positive rate across a wide range of settings and levels of illumination. Our fall detection method has the potential to dramatically improve the safety and well-being of vulnerable groups by giving immediate aid and support in the event that a fall occurs