Human Walking Behavior detection with a RGB-D Sensors Network for Ambient Assisted Living Applications

Automatically determining anomalies in human behavior is an important tool in ambient assisted living, especially when it concerns elderly people that for several reasons cannot be continuously monitored and assisted by a caregiver or a family member. This paper proposes a network of low cost RGB-D sensors with no overlapping fields-of-view, capable of identifying anomalous behaviors with respect a pre-learned normal one. A 3D trajectory analysis is carried out by comparing three different classifiers (SVM, neural networks and k-nearest neighbors). The results on real experiments prove the effectiveness of the proposed approach both in terms of performances and of real time application. Moreover, the possibility to extract and use depth information without considering color information enables the system to operate while respecting user privacy.