ID:24351
Type of Publication: Journal Articles
Authors: Asaf Shabtai,
Title: Sensor-based approach for predicting departure time of smartphone users
Name of the Journal: 2015 2nd ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)
Year: 2015
Pages: 146 - 7
Abstract: While location prediction of smartphone users has made great strides in recent years, a major challenge remains. As users spend the majority of their time is several fixed locations (home, work), existing algorithms are unable to identify the exact time in which a person is likely to depart from one place to another. In this work we present a sensor-based approach designed to predict the departure time of users. By using location and accelerometer sensors we were able to train a generic classification model that is able to predict whether the user will stay put or move to a different location with true positive rate of 0.73 and false positive rate of 0.3.
Keywords: accelerometers;learning (artificial intelligence);mobile computing;pattern classification;sensors;smart phones; ,
Last Updated: 1/14/2016 12:00:00 AM
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