Fall Detection by Wearable Device Using Support Vector Machine

Abstract

In this study, using simulated falls and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated with wearable tri-axial accelerometer sensors, mounted on the chest. The movement data of human body analysis was performed using one-class support vector machine (SVM) to determine the feature of motion types. Experiments to detect falls are performed in four directions: forward, backward, left, and right. The preliminary results show that this method can detect the falls effectively, reduces both false positives and false negatives, while improving fall detection accuracy, and the application can offer a new guarantee for the elderly health.