MoodScope: building a mood sensor from smartphone usage patterns

@inproceedings{Likamwa2013MoodScopeBA,
  title={MoodScope: building a mood sensor from smartphone usage patterns},
  author={Robert Likamwa and Yunxin Liu and Nicholas D. Lane and Lin Zhong},
  booktitle={MobiSys '13},
  year={2013}
}
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… 

Figures from this paper

Poster: Context-driven Mood Mining
  • R. Rana
  • Computer Science
    MobiSys '16 Companion
  • 2016
In the era of smartphones, there is almost an app for everything. Despite that smartphones are mostly unable to infer user’s mood. Mood inference can be useful for many applications, in particular
How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition
TLDR
MoA2, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level is presented, which combines benefits of state of the art approaches and is concluded by smartphone-based wearable sensing.
Moodbook: An Application for Continuous Monitoring of Social Media Usage and Mood
TLDR
A mobile application is proposed that records the social media interaction patterns of a user and captures their mood before and after each social media use, until it can automatically infer the mood of the user through theirsocial media interaction pattern.
What data are smartphone users willing to share with researchers?
TLDR
This work develops the Android app track your daily routine (TYDR), which tracks and records smartphone data and utilizes psychometric personality questionnaires, and introduces a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone.
Learning a Privacy-Preserving Global Feature Set for Mood Classification Using Smartphone Activity and Sensor Data
TLDR
This work shows that features representative of app, call, and text messaging patterns and previous levels of valence and arousal may be most useful for mood detection, and finds that the salient feature set only resulted in a 2% degradation in performance compared to the use of all features.
Mobile Mood Tracking
TLDR
The results suggest that the association between mood and stress generally depends on the measure of mood and its items, and the design of an adaptive mood measure that reduces the number of questions based on its prediction of user mood fluctuations is introduced.
Naturalistic Recognition of Activities and Mood Using Wearable Electronics
TLDR
This work engineer a recognition pipeline to recognize daily activities from commercially popularized wearable electronics and uses predicted activities to learn a regression model capable of assessing user mood, which outperforms with statistical significance and is validated for robustness against noise from activity misclassification.
Timing rather than user traits mediates mood sampling on smartphones
TLDR
Current mood surveys should be preferred for a higher sampling accuracy, while daily mood surveys are more suitable if compliance is more important, and design recommendations for mood sampling using smartphones are outlined.
HealthyOffice: Mood recognition at work using smartphones and wearable sensors
TLDR
A novel mood recognition framework that is able to identify five intensity levels for eight different types of moods every two hours is proposed, and a smartphone app is presented, designed to facilitate self-reporting in a structured manner and provide the ground truth.
A wearable system for mood assessment considering smartphone features and data from mobile ECGs
TLDR
This paper proposes to combine smartphones as a rich sensor system and smartwatches as a wearable heart rate monitor to serve as a platform for reporting mood states and assessed all three mood dimensions valence, energetic arousal and calmness.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 51 REFERENCES
Can Your Smartphone Infer Your Mood ?
Our driving vision is a smartphone service, called MoodSense, that can infer its owner’s mood based on information already available in today’s smartphones. The service will fundamentally enhance
A study of mobile mood awareness and communication through MobiMood
TLDR
The results highlight that certain contextual factors had an effect on mood and the interpretation of moods, and mood sharing and mood awareness appear to be good springboards for conversations and increased communication among users.
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research
TLDR
It is shown how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
Towards unobtrusive emotion recognition for affective social communication
TLDR
A machine learning approach to gather, analyze and classify device usage patterns, and a social network service client for Android smartphones which unobtrusively find various behavioral patterns and the current context of users are developed.
Towards Mood Based Mobile Services and Applications
TLDR
This paper presents the first prototype of a mobile system platform that is able to derive the mood of a person and make it available as a contextual building block to mobile services and application.
Mood meter: counting smiles in the wild
TLDR
A computer vision based system that automatically encouraged, recognized and counted smiles on a college campus and made them smile more than they expected, and it made them and others around them feel momentarily better.
Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones
TLDR
This constitutes the first study on the analysis and classification of personality traits using smartphone data and develops an automatic method to infer the personality type of a user based on cell phone usage using supervised learning.
Automatic mood detection and tracking of music audio signals
TLDR
A hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures, and has the advantage of emphasizing the most suitable features in different detection tasks.
Enabling large-scale human activity inference on smartphones using community similarity networks (csn)
TLDR
Community Similarity Networks (CSN) is proposed, which incorporates inter-person similarity measurements into the classifier training process and outperforms existing approaches to classifiers training under the presence of population diversity.
The Influence of Mood on Perceptions of Social Interactions
Abstract Interpreting our own and others' social behaviors is an important cognitive task in everyday life. Recent work in cognitive psychology suggests that temporary mood states may have a
...
1
2
3
4
5
...