@inproceedings{004de97a4c6e4b06a10a98db74144784,
title = "MoodScope: Building a mood sensor from smartphone usage patterns",
abstract = "We present MoodScope, a software system which infers the mood of its user based on how the smartphone is used. Similar to smart-phone sensors that measure acceleration, light, and other physical properties, MoodScope is a {"}sensor{"} that measures the mental state of the user and provides mood as an important input to context-aware computing. We run a formative statistical study with smartphone-logged data collected from 32 participants over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user's daily mood average with an accuracy of 93% after a two-month training period. Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user's mood.",
keywords = "Affective computing, Machine learning, Mood, Smartphone usage",
author = "Robert Likamwa and Yunxin Liu and Lane, {Nicholas D.} and Lin Zhong",
year = "2013",
doi = "10.1145/2462456.2483967",
language = "English (US)",
isbn = "9781450316729",
series = "MobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services",
pages = "465--466",
booktitle = "MobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services",
note = "11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2013 ; Conference date: 25-06-2013 Through 28-06-2013",
}