@inproceedings{8bc0cbc4093b4a7d8e27fead586df5fc,
title = "MoodScope: Building a mood sensor from smartphone usage patterns",
abstract = "We report a first-of-its-kind smartphone software system, MoodScope, which infers the mood of its user based on how the smartphone is used. Compared to smartphone 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 mood 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 initial accuracy of 66\%, which gradually improves to an accuracy of 93\% after a two-month personalized 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. We provide a MoodScope API for developers to use our system to create mood-enabled applications. We further create and deploy a mood-sharing social application.",
keywords = "Affective computing, Machine learning, Mobile systems, Mood, Smartphone usage",
author = "Robert Likamwa and Yunxin Liu and Lane, \{Nicholas D.\} and Lin Zhong",
year = "2013",
doi = "10.1145/2462456.2464449",
language = "English (US)",
isbn = "9781450316729",
series = "MobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services",
pages = "389--401",
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",
}