Document - Limb movements classification using wearable wireless transceivers

2011

Journal article Open Access

Limb movements classification using wearable wireless transceivers

Anda R. Guraliuc, Paolo Barsocchi, Francesco Potortì, Paolo Nepa

Biotechnology K-Nearest Neighbour (K-NN) Electrical and Electronic Engineering Computer Science Applications Support Vector Machine (SVM) General Medicine Received Signal Strength (RSS) Classification of human limbs activities

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

Source: IEEE transactions on information technology in biomedicine 15 (2011): 474–480. doi:10.1109/TITB.2011.2118763

Publisher: Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:199278,
	title = {Limb movements classification using wearable wireless transceivers},
	author = {Anda R.  Guraliuc and Paolo Barsocchi and Francesco Potortì and Paolo Nepa},
	publisher = {Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America},
	doi = {10.1109/titb.2011.2118763},
	journal = {IEEE transactions on information technology in biomedicine},
	volume = {15},
	pages = {474–480},
	year = {2011}
}