PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Guraliuc A. R., Barsocchi P., Potort́ F., Nepa P. Classification of human limb rehabilitation activities. Technical report, 2010.
 
 
Abstract
(English)
A feasibility study on using the received signal strength measured by small wireless transceivers in order to classify typical human body rehabilitation movements is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz incorporate a Received Signal Strength Indicator (RSSI): the key idea is to collect the RSSI values measured between a set of wireless devices strategically placed on a human body, specifically on upper and lower limbs, to monitor some typical rehabilitation activities. The collected RSSI data are processed using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) algorithms, in order to classify the rehabilitation activities.
Subject Physical rehabilitation
K-Nearest Neighbor (K-NN)
RSSI
Support Vector Machine (SVM)
Wearable wireless devices
B.4.1 Data Communications Devices
G.3 PROBABILITY AND STATISTICS


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