The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
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 gradu-ally improves to an accuracy of 93% after a two-month personal-ized 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.
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Metadata
- Authors
- LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L.
- Affiliations
- Rice University (US)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceeding of the 11th annual international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2013-06-25
- Pages
- 389-402
- OpenAlex
- W2152423878
- Cited by
- 444
- References
- 39
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 15 · 2023: 12 · 2022: 34 · 2021: 29 · 2020: 53 · 2019: 65 · 2018: 37 · 2017: 59 · 2016: 51 · 2015: 41 · 2014: 23 · 2013: 6
- FWCI
- 25.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile Systems, Applications, and Services (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- smartphone usage patterns, context-aware computing, individualized training, smartphone sensors
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