Real-time fall detection and emergency alert system for elderly individuals

Computer Engineering

Falls among the elderly pose a major health concern, often leading to loss of independence, fear of falling, and significant hospitalization costs. Over the past two decades, medical institutions and researchers have conducted extensive studies on falls, resulting in the development of various scientific solutions and commercial products. Traditional fall detection systems utilize different sensing methods to identify falls after they occur. However, recent research has shifted towards fall prevention, focusing on predicting falls before they happen. Despite advancements, early fall prediction systems are still primarily limited to clinical investigations, and existing studies lack a standardized classification framework. This study aims to bridge this gap by providing a comprehensive overview of falls in the elderly and proposing a generic classification of fall-related systems based on sensor deployment. Using this classification, an in-depth study covering both fall detection and prevention methods has been carried out, including various data processing techniques. The primary objective of this research is to improve the understanding of fall-related systems among medical technologists in public health, thereby aiding in the development of more effective fall-prevention strategies. By integrating advanced sensing technologies and data analysis techniques, this study seeks to enhance the accuracy and reliability of fall detection and prevention methods, ultimately reducing the impact of falls on the elderly population.

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