Fall Detection and Prevention Systems: Sensor Type Perspective

Falls among older adults pose significant health risks, making their prevention and detection critical areas of research. This review examines fall detection and prevention systems, categorizing them based on sensor types and utilization methods: wearable sensors, environmental sensors, radio-frequency-based sensors, and hybrid systems. Additionally, it explores the methods employed within these systems. Given the limitations of traditional linear approaches in accurately detecting falls, recent research emphasizes artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), to enhance detection accuracy and system functionality. The review provides an overview of the sensors and algorithms used in fall detection and prevention systems, alongside their outcomes. Key findings and challenges related to specific sensors and systems are discussed in detail. This analysis offers researchers a comprehensive understanding of current technologies, highlights the contributions of AI methods, and outlines potential future directions in the field. By evaluating sensors, methodologies, and system sensitivities, the aim is to contribute to the development of effective solutions tailored to specific sensitivities.