MoodScope: building a mood sensor from smartphone usage patterns

  • Authors:

  • Robert LiKamWa;Yunxin Liu;Nicholas D. Lane;Lin Zhong

  • Affiliations:

  • Rice University, Houston, Texas, USA;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Rice University, Houston, Texas, USA

  • Venue:
  • Proceeding of the 11th annual international conference on Mobile systems, applications, and services
  • Year:
  • 2013

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Abstract

We present MoodScope, a software system which infers the mood of its user based on how the smartphone is used. Similar 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 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.