title: Position recognition to support bedsores prevention.
abstract: In this paper, a feasibility study where small wireless devices are used to classify some typical users 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 peoples bodies in tomorrows pervasive computing environments. The key idea of this work 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 users 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 neighbour methods, in order to recognize the different users position, and thus supporting the bedsores issue.
authors.forename: P
authors.initials: P
authors.lastname: Barsocchi
mesh_terms: D001513:Beds; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011187:Posture / Q000502:physiology*; D003668:Pressure Ulcer / Q000517:prevention & control*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D059015:Wireless Technology / Q000295:instrumentation*