DEEP LEARNING-BASED HUMAN DETECTION FOR FALL INJURIES

Abstract

Falls are a significant public health problem, especially among the elderly and people with limited mobility. A fall may seem like a minor accident, but the injuries that can result from a fall and the underlying health problems that can cause falls have a significant impact on people's lives. Especially in elderly individuals, such accidents occur more frequently and lead to more severe consequences. Research shows that one-third of homebound older adults and more than half of hospitalized older adults are at risk for falls. Falls can result in impaired balance and gait, fear of falling, disability, and a decline in daily activities and quality of life. This fear adversely affects the daily lives of elderly individuals. Therefore, real-time fall detection systems contribute to preventing more severe injuries. Our proposed method uses state-of-the-art deep learning techniques to detect and localize people in video streams. Its goal is to ensure the rapid provision of assistance to the person who has fallen after the incident. In the development stage of the paper, YOLOv7 and YOLOv8 architectures have been utilized. Furthermore, we discuss the potential applications of this approach in real-world scenarios, such as fall detection systems for elderly care, surveillance, and automated emergency response. The main contributions of this work are a novel deep learning approach to human detection in the context of fall injuries, practical applications of the proposed approach, and its potential to improve safety and quality of life for at-risk populations, especially the elderly and those with limited mobility.

Keywords

• YOLOv7
• YOLOv8
• Elderly People
• CNN
• Fall Detection.

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