An enhanced fall detection system for elderly person monitoring using consumer home networks

TLDR

Various fall‑detection solutions have been proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity, and specificity. The study proposes an enhanced fall detection system for elderly monitoring using body‑worn smart sensors and consumer home networks. The system employs body‑worn accelerometer and cardiotachometer sensors with treble thresholds to log fall impacts and distinguish them from normal activities via consumer home networks. In a prototype tested with 30 healthy participants, the system achieved 97.5 % accuracy, 96.8 % sensitivity, and 98.1 % specificity, demonstrating reliable performance for consumer deployment.

Abstract

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate.