MoodScope: Building a mood sensor from smartphone usage patterns

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

Volumn , Issue , 2013, Pages 389-401

  Likamwa, Robert ^a,b   Liu, Yunxin ^b   Lane, Nicholas D ^b   Zhong, Lin ^a  

^a RICE UNIVERSITY   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Affective computing; Machine learning; Mobile systems; Mood; Smartphone usage

Indexed keywords

AFFECTIVE COMPUTING; COMMUNICATION HISTORY; CONTEXT-AWARE COMPUTING; MOBILE SYSTEMS; MOOD; SOCIAL APPLICATIONS; SOFTWARE SYSTEMS; USAGE PATTERNS;

LEARNING SYSTEMS; SENSORS; SIGNAL ENCODING;

SMARTPHONES;

EID: 84881120604     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2462456.2464449     Document Type: Conference Paper

References (40)

1. J. A. Russell. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), pp. 1161-1178, 1980.
2. C. D. Batson, L. L. Shaw and K. C. Oleson. Distinguishing affect, mood and emotion: toward functionally-based conceptual distinctions. Review of Personality and Social Psychology, 13, Newbury Parks, 1992.
3. P. Ekman. Universal and cultural differences in facial expressions of emotion. In Proc. Nebraska Symp. Motivation, 1971.
4. S. S. Tomkins. Affect, Imagery, Consciousness. Springer, 1962.
5. J. A. Russell. Affect grid: a single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 1989.
6. J. Tao and T. Tan. Affective computing: a review. Affective Computing and Intelligent Interaction, LNCS 3784, Springer, 2005.
7. M. Yik, Studying Affect among the Chinese: The circular way. Journal of Personality Assessment, 91(5), 2009.
8. K. Church, E. Hoggan and N. Oliver. A study of mobile mood awareness and communication through MobiMood. In Proc. NordiCHI, 2010.
9. A. Gluhak, M. Presser, L. Zhu, S. Esfandiyari and S. Kupschick. Towards mood based mobile services and applications. In Proc. European Conference on Smart Sensing & Context (EuroSSC), 2007.
10. D. Watson, L.A. Clark, and A. Tellegen. Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, pages 1063-1070, 1988.
11. J. R. Crawford and J. D. Henry. The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43, pages 245-265, 2004.
12. J. T. Larsen, A. P. McGraw and J. T. Cacioppo, Can people feel happy and sad at the same time? Journal of Personality and Social Psychology, 81(4), 1988.
13. Z. Zeng, M. Pantic, G. I. Roisman and T. S. Huang. A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 2009.
14. A. Vinciarelli, M. Pantic and H. Bourlard. Social signal processing: survey of an emerging domain. Image and Vision Computing, 2009.
15. J. F. Cohn. Foundations of human computing: Facial expression and emotion. In Proc. ACM Int'l Conf. Multimodal Interfaces (ICMI), 2006.
16. M. Pantic and M.S. Bartlett. Machine analysis of facial expressions. Face Recognition, K. Delac and M. Grgic, eds., pages 377-416, I-Tech Education and Publishing, 2007.
17. P. Ekman, W.V. Friesen, and J.C. Hager. Facial action coding system. A Human Face, 2002.
18. C.M. Lee and S.S. Narayanan. Toward detecting emotions in spoken dialogs. IEEE Trans. Speech and Audio Processing, 13(2), pages 293-303, 2005.
19. B. Schuller, R.J. Villar, G. Rigoll, and M. Lang. Meta-classifiers in acoustic and linguistic feature fusion-based affect recognition. In Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing (ICASSP '05), pages 325-328, 2005.
20. R. W. Picard. Affective computing, MIT Press, 1997.
21. The Affectiva Q sensor, http://www.affectiva.com/q-sensor/.
22. A. Gluhak, M. Presser, L. Zhu, S. Esfandiyari and S. Kup-schick. Towards mood based mobile services and applications. In Proc. European Conference on Smart Sensing & Context (EuroSSC), 2007.
23. K. K. Rachuri, M. Musolesi, C. Mascolo, P. J. Rentfrow, C. Longworth and A. Aucinas. EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In Proc. ACM Int'l Conf. on Ubiquitous Computing (UbiComp), 2010.
24. H. Lee, Y. Choi, S. Lee and I. P. Park. Towards unobtrusive emotion recognition for affective social communication. In Proc. IEEE Consumer Communications & Networking Conference (CCNC), 2012.
25. The mappiness application, http://www.mappiness.org.uk/.
26. The gottaFeeling application, http://gottafeeling.com/.
27. C. Shepard, A. Rahmati, C. Tossell, L. Zhong and P. Kortum. LiveLab: measuring wireless networks and smartphone users in the field. ACM SIGMETRICS Perform. Eval. Rev., 38(3), 2010.
28. J. P. Forgas, G. H. Bower and S.E. Krantz. The influence of mood on perceptions of social interactions, Journal of Experimental Social Psychology, 20(6), 1984.
29. L. A. Clark and D. Watson. Mood and the mundane: Relations between daily life events and self-reported mood. Journal of Personality and Social Psychology, 54(2), 1988.
30. M. Ester, H.-P. Kriegel, J. Sander and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. Second International Conference on Knowledge Discovery and Data Mining, 1996.
31. C. J. Beedie, P. C. Terry and A. M. Lane. Distinctions between emotion and mood. Cognition and Emotion, 19(6), 2005.
32. Hernandez, Javier, et al. Mood meter: counting smiles in the wild. In Proc. ACM Int'l Conf. on Ubiquitous Computing (UbiComp), 2012.
33. G. Chittaranjan, J. Blom and D. Gatica-Perez. Who's who with Big-Five: Analyzing and Classifying personality traits with smartphones. In Proc. Int'l Symposium on Wearable Computers (ISWC), 2011.
34. L. Lu, D. Liu and H. Zhang. Automatic mood detection and tracking of music audio signals. IEEE Transactions on Audio, Speech and Language Processing, 14(1), 2006.
35. N. Japkowicz and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 2002.
36. A. Jain and D. Zongker. Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 1997.
37. A. Narayanan and V. Shmatikov. Myths and fallacies of Personally Identifiable Information. From Commun. ACM 53(6), pages 24-26, June 2010.
38. N. D. Lane, Y. Xu, H. Lu, S. Hu, T. Choudhury, A. T. Campbell and F. Zhao. Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In Proc. ACM Int'l Conf. on Ubiquitous Computing (UbiComp), 2011.
39. R. LiKamWa, Y. Liu, N. D. Lane and L. Zhong. Can your smartphone infer your mood? In Proc. PhoneSense Workshop, 2011.
40. Hong Lu, A. J. Bernheim Brush, Bodhi Priyantha, Amy K. Karlson, and Jie Liu. SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones. In Proc. Int'l Conf. on Pervasive Computing, 2011.