Detection and classification of human arm movements for physical rehabilitation

Author

Guraliuc, A.R. ; Serra, A.A. ; Nepa, P. ; Manara, G. ; Potorti, F.

Author_Institution

Dept. of Inf. Eng., Univ. of Pisa, Pisa, Italy

fYear

2010

fDate

11-17 July 2010

Firstpage

1

Lastpage

4

Abstract

In this paper an arm rehabilitation scenario was set-up to estimate and evaluate the principal activities of this limb by the suggested WBAN-based approach. A couple of wearable sensors mounted on the arm and a fixed node constitute the wireless network. In order to identify and classify the rehabilitation activities an algorithm based on the Received Signal Strength Indicator (RSSI), a parameter already available in the wireless sensor nodes, was applied. As a first attempt, a support vector machine (SVM) learning technique was implemented.

Keywords

biomechanics; biomedical measurement; patient rehabilitation; support vector machines; wearable computers; wireless sensor networks; WBAN-based approach; fixed node; human arm movements; limb; physical rehabilitation; received signal strength indicator; support vector machine; wearable sensors; wireless network; wireless sensor nodes; Ad hoc networks; Biomedical monitoring; Discrete Fourier transforms; Sensors; Support vector machines; Training; Wireless sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Antennas and Propagation Society International Symposium (APSURSI), 2010 IEEE

Conference_Location

Toronto, ON

ISSN

1522-3965

Print_ISBN

978-1-4244-4967-5

Type

conf

DOI

10.1109/APS.2010.5561051

Filename

5561051