Prediction of User Context Using Smartphone Activity Data
Abstract
When a person seeks another persons attention, it is of prime importance to assess how interruptible the other person is. Since smartphones are ubiquitously used as communication media these days, interruptibility prediction on smartphones has started to attract great interest from both academia and industry. Previous studies, in general, attempted to model interruptibility using the behaviors at the current moment and in the immediate past (e.g., 5 minutes before). However, a persons interruptibility at a certain moment is indeed affected by his/her preceding behaviors for several reasons. Motivated by this long-term effect, in this project we propose a novel methodology of extracting features based on past behaviors from smartphone sensor data. The primary difference from previous studies is that we systematically consider a longer history of up to a day in addition to the current point and the immediate past. To represent behaviors in a day accurately and compactly, our methodology divides a day into multiple timeslots and then, for each timeslot, derives relevant features such as the temporal shapes of the time series of the sensor data. In order to verify the advantage of our methodology, we collected a data set of smartphone usage from 25 participants for four weeks and obtained a license to a large-scale public data set constructed from 907 users over approximately 9 months. The experimental results on the two data sets show that looking back to the beginning of the current day improves prediction accuracy by up to 13 and 8 , respectively, compared with the baseline and state-of-the-art methods.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 22, 2016
- Accession Number
- AD1022787
Entities
People
- Jae-Gil Lee
Organizations
- KAIST