Fall Detection on Mobile Phones Using Features from a Five-Phase Model

The injuries caused by falls are great threats to the elderly people. With the ability of communication and motion sensing, the mobile phone is an ideal platform to detect the occurrence of fall accidents and help the injured person receive first aid. However, the missed detection and false alarm of the monitoring software will cause annoyance to the users in real use. In this paper, we present a novel fall detection technique using features from a five-phase model which describes the state change of the user's motion during the fall. Experiment results validate the effectiveness of the algorithm and show that the features derived from the model as gravity-cross rate and non-primarily maximum and minimum points of the acceleration data are useful to improve the precision of the detection. Moreover, we implement the technique as uCare, an Android application that helps elderly people in fall prevention, detection and first aid seeking.