Support Vector Machine for tri-axial accelerometer-based fall detector

The aim of this work is the development of a computationally low-cost scheme for feature extraction and the implementation of an One-class Support Vector Machine classifier for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer, managed by a stand-alone PC through ZigBee connection. The proposed approach allows the generalization of the detection of fall events in several practical conditions after a short period of calibration. The approach appears invariant to age, weight, people's height and the relative positioning area (even in the upper part of the waist) This overcomes the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used, while maintaining high performances in terms of specificity and sensitivity.