DocumentCode :
3728360
Title :
Analysis of Actual Smartphone Logs for Predicting the User´s Routine Settings of Application Volume
Author :
Tatsuhito Hasegawa;Makoto Koshino;Haruhiko Kimura
Author_Institution :
Div. of Healthcare Inf., Tokyo Healthcare Univ., Tokyo, Japan
fYear :
2015
Firstpage :
2654
Lastpage :
2659
Abstract :
Although the volume settings of smartphones are important for users, they still need to push the hardware sound button manually. The purpose of our study is to improve the usability of volume settings. Our proposed method predicts the user´s routine volume settings by learning the actual daily smartphone logs. Related works used suitable volume settings input by the experimental participants to learn the volume setting pattern for each user. In contrast, this study uses actual smartphone logs. This paper describes three results of the analyses of many actual smartphone logs. First, we investigate the rate at which the users change the application volume. Second, we examine the accuracy of the results predicted by our method. Third, we classify the test users as users for whom our method effectively works or others. Finally, we discuss the appropriateness of the predicted results for users´ routine settings.
Keywords :
"Turning","Motion pictures","Machine learning algorithms","Batteries","Games","Smart phones","Switches"
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/SMC.2015.464
Filename :
7379596
Link To Document :
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