Fall Detection by Wearable Sensor and One-Class SVMAlgorithm

摘要

The fall is a crucial problem in the elderly people’s daily life, and thernearly detection of fall is very important to rescue the subjects and avoid thernbadly prognosis. In this paper, we use a wearable tri-axial accelerometer to capturernthe movement data of human body, and propose a novel fall detectionrnmethod based on one-class support vector machine (SVM). The one-class SVMrnmodel is trained by the positive samples from the falls of younger volunteersrnand a dummy, and the outliers from the non-fall daily activities of younger andrnthe elderly volunteers. The preliminary results show that this method can detectrnthe falls effectively, and reduce the probability of being damaged in the experimentsrnfor the elderly people.