Position Recognition to Support Bedsores Prevention
In this paper, a feasibility study where small wireless devices are used to classify some typical user's positions in the bed is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The key idea of this study is to leverage their presence by collecting the received signal strength (RSS) measured among fixed devices, deployed in the environment, and the wearable one. The RSS measurements are used to classify a set of user's positions in the bed, monitoring the activities of patients unable to make the desirable bodily movements. The collected data are classified using both support vector machine and K-nearest neighbor methods, in order to recognize the different user's position, and thus supporting the bedsores issue.
bedsores prevention received signal strength (rss) classification of user's positions in the bed k-nearest neighbor (k-nn) support vector machine (svm)