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Proceedings Paper

Human movement activity classification approaches that use wearable sensors and mobile devices
Author(s): Sahak Kaghyan; Hakob Sarukhanyan; David Akopian
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Paper Abstract

Cell phones and other mobile devices become part of human culture and change activity and lifestyle patterns. Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. Latest generations of smart phones incorporate GPS and WLAN location finding modules, vision cameras, microphones, accelerometers, temperature sensors etc. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. This paper reviews different approaches of human activity recognition.

Paper Details

Date Published: 7 March 2013
PDF: 12 pages
Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86670O (7 March 2013); doi: 10.1117/12.2007868
Show Author Affiliations
Sahak Kaghyan, Armenian-Russian (Slavonic) Univ. (Armenia)
Hakob Sarukhanyan, Institute for Informatics and Automation Problems (Armenia)
David Akopian, The Univ. of Texas at San Antonio (United States)


Published in SPIE Proceedings Vol. 8667:
Multimedia Content and Mobile Devices
Reiner Creutzburg; Todor G. Georgiev; Dietmar Wüller; Cees G. M. Snoek; Kevin J. Matherson; David Akopian; Andrew Lumsdaine; Lyndon S. Kennedy, Editor(s)

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