MoodScope: Building a mood sensor from smartphone usage patterns

Robert Likamwa, Yunxin Liu, Nicholas D. Lane, Lin Zhong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

170 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services
Pages465-466
Number of pages2
DOIs
StatePublished - Aug 12 2013
Externally publishedYes
Event11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2013 - Taipei, Taiwan, Province of China
Duration: Jun 25 2013Jun 28 2013

Publication series

NameMobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services

Other

Other11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2013
Country/TerritoryTaiwan, Province of China
CityTaipei
Period6/25/136/28/13

Keywords

  • Affective computing
  • Machine learning
  • Mood
  • Smartphone usage

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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