Determining Fall direction and severity using SVMs

Fall detection has been an important consideration in the field of human activity recognition and has garnered significant interest from researchers. A typical aim within fall detection systems is the determination of whether a fall has occurred or not. However, less attention has been provided to the problem of fall direction detection and severity. In this paper, we experiment with the detection of direction and severity in falls using the SisFall dataset. We perform this by using a combination of time and frequency domain features on inertial measurement sensor values along with a Support Vector Machine classifier. We are able to achieve promising results for the considered task.