A smart phone-based pocket fall accident detection system

Abstract

A smart phone-based pocket fall accident detection system is proposed in this paper. To realize the system, the angles acquired by the electronic compass and the waveform sequence of the triaxial accelerometer on the smart phone are used as the input signals of the proposed system. The acquired signals are then used to generate an ordered feature sequence and examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current stage, it can proceed to next stage; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. With the proposed cascade classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished fall detection accuracy up to 96% on the sensitivity and 99.71% on the specificity can be obtained when a set of 400 test actions in eight different kinds of activities are estimated by using the proposed approach, which justifies the superiority of the proposed algorithm.