Supervised wearable wireless system for fall detection

Falling down events can cause trauma, disability and death among older people. Accelerometer-based devices are able to detect falls in controlled environments. This kind of solution often presents poor performance in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a Machine Learning scheme for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people and to the relative positioning area (even in the upper part of the waist), overcoming 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 performance in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an One-Class Support Vector Machine classifier in a stand-alone PC.