Improving fall detection by the use of depth sensor and accelerometer

Since falls are a major cause of harm to older people, there is considerable demand for low-cost fall detection systems. To meet demands of the end-users we propose a new architecture for low cost and reliable fall detection, where an accelerometer is used to indicate a potential fall and the Kinect sensor is used to authenticate the eventual fall alert. In consequence, the depth maps are not processed frame-by-frame, but instead we download from a circular buffer a sequence of depth maps acquired prior to the fall and then process them to authenticate fall event. We determine features both in the depth maps and point clouds to extract discriminative fall descriptors. Since people typically follow typical motion patterns related to specific locations in home or typical daily activities, we propose to utilize k-nn classifier to implement an exemplar-based fall detector. We show that such a classifier is competitive on our publicly available URFD dataset in terms of sensitivity and specificity while being much more simple to implement on an embedded platform.