Mobile Phone Based Falling Detection Sensor and Computer-Aided Algorithm for Elderly People

Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, South Korea

Abstract

Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.