PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Gurialiuc A. R., Barsocchi P., Potort́ F., Nepa P. Limb movements classification using wearable wireless transceivers. In: IEEE Transactions on Information Technology in Biomedicine, vol. 15 (3) pp. 474 - 480. IEEE, 2011.
 
 
Abstract
(English)
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 using bot{Support Vector Machine} and {K-Nearest Neighbour} methods, in order to recognise the different activities
URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5720315
DOI: 10.1109/TITB.2011.2118763
Subject Classification of human limbs activities
K-Nearest Neighbour (K-NN)
Received Signal Strength (RSS)
Support Vector Machine (SVM)
Wearable wireless devices
C.3 SPECIAL-PURPOSE AND APPLICATION-BASED SYSTEMS - Real-time and embedded systems
G.3 PROBABILITY AND STATISTICS. Nonparametric statistics
I.2.6 Learning


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