title: Limb movements classification using wearable wireless transceivers.
abstract: A feasibility study, where small wireless transceivers are used to classify some typical limb movements used in physical therapy processes 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 work is to exploit their presence by collecting the received signal strength measured between those worn by a person. The measurements are used to classify a set of kinesiotherapy activities. The collected data are classified by using both support vector machine and K-nearest neighbor methods, in order to recognise the different activities.
authors.affiliation: Department of Information Engineering, University of Pisa, via Caruso 16, I-56122, Pisa, Italy. anda.guraliuc@iet.unipi.it
authors.forename: Anda R
authors.initials: AR
authors.lastname: Guraliuc
authors.forename: Paolo
authors.initials: P
authors.lastname: Barsocchi
authors.forename: Francesco
authors.initials: F
authors.lastname: Potortì
authors.forename: Paolo
authors.initials: P
authors.lastname: Nepa
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003020:Clothing; D005121:Extremities / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009068:Movement / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013686:Telemetry / Q000295:instrumentation*