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Title of Journal: Pers Ubiquit Comput

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Abbravation: Personal and Ubiquitous Computing

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Springer London

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DOI

10.1016/0039-9140(65)80225-9

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1617-4917

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Activity recognition for creatures of habit

Authors: Dawud Gordon Jürgen Czerny Michael Beigl
Publish Date: 2013/03/03
Volume: 18, Issue: 1, Pages: 205-221
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Abstract

Energy storage is quickly becoming the limiting factor in mobile pervasive technology We introduce a novel method for activity recognition which leverages the predictability of human behavior to conserve energy by dynamically selecting sensors We further present a taxonomy of existing approaches to dynamically reducing consumption while maintaining recognition rates The novel algorithm conserves energy by quantifying activitysensor dependencies and using prediction methods to identify likely future activities The approach is implemented and simulated using two activity recognition data sets and the effects of the novel method are evaluated in terms of recognition rates energy consumption and prediction rates The results indicate that switching off sensors only significantly affects prediction under extreme conditions and that these effects can be counteracted by adjusting system parameters Large savings in energy can be achieved at very low cost for example recognition losses of 15 pp with 848  energy savings for the first data set and 28 pp and 899  for the second

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