PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Barsocchi P. Position recognition to support bedsores prevention. In: IEEE Journal of Biomedical and Health Informatics (giÓ IEEE Transactions on Information Technology in Biomedicine), vol. 17 (1) pp. 53 - 59. IEEE, 2013.
 
 
Abstract
(English)
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.
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6310061&contentType=Journals+%26+Magazines&queryText%3DPosition+Recognition+to+Support+Bedsores+Prevention
DOI: 10.1109/TITB.2012.2220374
Subject Classification of user's positions in the bed
Received Signal Strength (RSS)
Support Vector Machine (SVM)
Bedsores prevention
K-Nearest Neighbour (K-NN)
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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