By Kun Li, Man Lu, Fenglong Lu, Qin Lv, Li Shang, Dragan Maksimovic (auth.), Judy Kay, Paul Lukowicz, Hideyuki Tokuda, Patrick Olivier, Antonio Krüger (eds.)
This booklet constitutes the refereed complaints of the tenth overseas convention on Pervasive Computing, Pervasive 2012, held in Newcastle, united kingdom, in June 2012. The 28 revised papers offered have been conscientiously reviewed and chosen from 138 submissions. The contributions are grouped into the subsequent topical sections: task taking pictures; city mobility and computing; domestic and effort; HCI; improvement instruments and units; indoor position and positioning; social computing and video games; privateness; public screens and services.
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LNCS, vol. 5538, pp. 25–41. Springer, Heidelberg (2009) 15. : A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010) 16. : Largescale battery system modeling and analysis for emerging electric-drive vehicles. In: ISLPED 2010, pp. 277–282 (2010) 17. : Regional driving characteristics, regional driving cycles. Transportation Research Part D 8, 361–381 (2003) 18. : The jigsaw continuous sensing engine for mobile phone applications. In: SenSys 2010 (2010) 19. : Pocket, bag, hand, etc.
We evaluated our method by using sensor data obtained from three real houses and achieved very high identification and recognition accuracies. Keywords: Sensors, Electrical devices, Battery. 1 Introduction Daily activity recognition is one of the most important tasks in pervasive computing applications because it has a wide range of uses in, for example, supporting the care of the elderly, lifelogging, and home automation [1,2]. Many studies have employed body-worn accelerometers to recognize human activities [3,4,5].
When a user selected the FF or RW button, we observed a noise corresponding to seeking the next/previous track. It also took several seconds for the player to stop operating. The lower graph in Fig. 2 (c) shows a frequency spectrogram. As shown in these graphs, the sensor data from the CD player were time varying, and so we should model temporal changes in the sensor data to recognize electrical events related to the CD player. Some portable electrical devices such as electric toothbrushes, electric shavers, handheld cleaners, and electric screwdrivers include motors.