Smartphone Based Data Mining for Fall Detection

Falls can be devastating to the affected individual, yet a common event and hence one of the major causes of injury or disability within the aged population in Malaysia and worldwide. This paper aims to detect human fall utilizing the built inertial measurement unit (IMU) sensors of a smartphone attached to the subject's body with the signals wirelessly transmitted to remote PC for processing. Matlab's mobile and the Smartphone Sensor Support is used to acquire the data from the smartphone which is then analysed to design an algorithm for the detection of fall. Falls in human are usually characterized by large acceleration. However, focusing only on a large value of the acceleration can result in many false positives from fall-like activities such as sitting down quickly and jumping. Thus, in this work, a threshold based fall detection algorithm is implemented while a supervised machine learning algorithm is used to classify activity daily living (ADL). This combination has been found effective in increasing the accuracy of the fall detection. The aim is to develop and verify the high precision detection algorithm using Matlab Simulink, followed by a few real time testing.