Title :
A lightweight and low-power activity recognition system for mini-wearable devices
Author_Institution :
Beijing Key Lab. of Mobile Comput. & Pervasive Device, Beijing, China
Abstract :
Recent years, miscellaneous mini-wearable devices (e.g. wristbands, wristwatches, armbands) have emerged in our lives to recognize daily activities for the users. Owning to the limitations of hardware, Activity Recognition (AR) models running inside the device are bound to certain challenges, such as processing power, storage capability and battery life. This paper proposes an activity recognition system by considering three limitations above, and a model generation framework to construct AR models which are lightweight in different phases in model generation.
Keywords :
mobile computing; pattern recognition; power aware computing; storage management; wearable computers; AR models; activity recognition system; armbands; battery life; mini-wearable devices; model generation; processing power; storage capability; wristbands; wristwatches; Decision support systems; Handheld computers; Pervasive computing; battery consumption; energy efficient learning; ligthtweight; space complexity; time complexity;
Conference_Titel :
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on
Conference_Location :
Budapest
DOI :
10.1109/PerComW.2014.6815189
