Paper
7 March 2013 Human movement activity classification approaches that use wearable sensors and mobile devices
Sahak Kaghyan, Hakob Sarukhanyan, David Akopian
Author Affiliations +
Proceedings Volume 8667, Multimedia Content and Mobile Devices; 86670O (2013) https://doi.org/10.1117/12.2007868
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sahak Kaghyan, Hakob Sarukhanyan, and David Akopian "Human movement activity classification approaches that use wearable sensors and mobile devices", Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86670O (7 March 2013); https://doi.org/10.1117/12.2007868
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CITATIONS
Cited by 7 scholarly publications and 2 patents.
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KEYWORDS
Sensors

Detection and tracking algorithms

Mobile devices

Video

Data modeling

Gyroscopes

Data processing

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