A lightweight and low-power activity recognition system for mini-wearable devices

Author

Lisha Hu

Author_Institution

Beijing Key Lab. of Mobile Comput. & Pervasive Device, Beijing, China

fYear

2014

fDate

24-28 March 2014

Firstpage

166

Lastpage

167

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on

Conference_Location

Budapest

Type

conf

DOI

10.1109/PerComW.2014.6815189

Filename

6815189