Research of Fall Detection and Fall Prevention Technologies: A Review

TL;DR

This review explores technologies for detecting and preventing falls, focusing on wearable and unobtrusive sensors to improve safety for older adults.

Contribution

The paper categorizes and evaluates fall detection methods, emphasizing the integration of multiple sensor technologies for improved accuracy.

Findings

Wearable sensors like accelerometers and EMG are effective for real-time fall prediction.

Unobtrusive systems such as LiDAR and depth sensors offer non-intrusive fall monitoring.

Combining sensor technologies enhances detection accuracy and response times.

Abstract

Falls represent a significant global public health issue, particularly among adults over the age of 60. This comprehensive review aims to provide an in-depth examination of current fall detection and prevention technologies. The study categorizes fall detection methods into pre-fall prediction and post-fall detection, using both wearable and unobtrusive sensors. Wearable technologies, such as accelerometers, gyroscopes, and electromyography (EMG) sensors, are explored for their efficacy in real-time fall prediction and detection. Unobtrusive methods, including camera-based systems, LiDAR, radar, ultrasonic sensors, and depth sensors, are evaluated for their ability to monitor falls without intruding on users’ daily activities. The integration of these technologies into healthcare settings is also discussed, with an emphasis on the importance of immediate response to fall events. By analyzing the operational principles, technological advancements, and practical applications of these systems, promising directions for future research and innovation in fall detection and prevention are identified. The findings highlight the need for multifaceted approaches combining various sensor technologies to enhance fall detection accuracy and response times, ultimately improving patient safety and quality of life.

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