ARD

Accidental fall is one of the major factors threatening the health of the elderly, which promotes the considerable development of fall detection technology. In our study, a novel method is proposed to detect falls prior to impact during walking. In terms of data collection, acceleration and angular velocity data are collected using a single sensor. By extracting distinctive features, our design goal is to accurately identify fall behavior at an early stage. To improve detection accuracy and reduce false alarms, a classifier based on the joint feature of accelerations and Euler angles (JFAE) analysis is developed. With the support vector machine (SVM) classifier, human activities are classified into eight categories: going upstairs, going downstairs, running, walking, falling forward, falling backward, falling left, and falling right. Not only can it achieve a sensitivity of 96.8% and precision of 96.75%, but also the method we proposed can achieve a high accuracy for the classifier. Compared with the method based on single feature, the method based on multiple features achieves a better performance. The preliminary results indicate that our study has potential application in a fall injury prevention system.