Scalable Health Labs

Mobile Bio-Behavioral Sensing

Moodscope

Harnessing Digital Interactions to Understand Mental State

MoodScope: Building a Mood Sensor from Smartphone Usage Patterns,” Proceedings of MobiSys, 2013.

TEAM MEMBERS:

Robert LiKamWa, Graduate Student, ECE, Rice University

Yunxin Liu, Microsoft Research Asia, Beijing, China

Nicholas Lane, Microsoft Research Asia, Beijing, China

Lin Zhong, Professor, ECE, Rice University

THE PROBLEM: Mood is an affective state that plays a significant role in our lives, influencing our behavior, driving social communication, and shifting our consumer preferences. But in the digital realm of mobile devices, there is a distinct lack of knowledge about mood unless manually provided. While devices have many sensors to understand the physical world around them, they are unable to develop insight about the object that matters the most: the user.

OUR SOLUTION: 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.

Daily mood averages and model estimations

 

 

 

Pleasure raining accuracy vs. training data size