Transcript Wearable Monitoring for Mood Recognition in Bipolar Disorder

Chao Li, Zhiyong Feng, Chao Xu
International Conference on Smart Computing,2014
Chairman: Shih-Chung Chen
Presenter: Chung-Yi Li
Advisor: Dr. Chun-Ju Hou
Date:2015/12/02
Outline
Introduction
Related Work
Analysis of Group-Based Model
Experiment Results
Conclusion
References
Introduction
Emotion Recognition offers a smooth interface
between humans and computers in the field of
HCI, which enables machines to understand
human emotions correctly.
Physiological signals, which is considered as
embodiment in emotion, have its advantages for
emotion recognition.
Introduction
There are two widely-adopted approaches for
general modeling in emotion recognition.
 Human’s social attribute
 User-independent modeling
Introduction
Based on this, this paper proposes a Group-
Based IRS model for user-independent system.
Conclusion
This paper collects affective physiological data
from 11 subjects in four emotions to investigate
the influence of IRS towards physiologicalbased emotion recognition in user-independent
scenario.
The result validates the effectiveness of groupbased IRS model where the recognition rate is
higher than general model for majority of
algorithms.
References

















[1] R. A. Calvo and S. D’Mello, “Affect detection: An interdisciplinary review of models, methods, and their applications,” IEEE Transactions on Affective Computing,
vol. 1, no. 1, pp. 18–37, 2010.
[2] O. AlZoubi, S. K. D’Mello, and R. A. Calvo, “Detecting naturalistic expressions of nonbasic affect using physiological signals,” IEEE Trans- actions on Affective
Computing, vol. 3, no. 3, pp. 298–310, 2012.
[3] J. Kim, “Bimodal emotion recognition using speech and physiological changes,” Robust Speech Recognition and Understanding, pp. 265–280, 2007.
[4] J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, J. J. Gross, M. E. Jabon, C. A. Hutcherson, C. Nass, and O. John, “Real-time classification of evoked emotions using
facial feature tracking and physiological responses,” International journal of human-computer studies, vol. 66, no. 5, pp. 303–317, 2008.
[5] F. Nasoz, C. L. Lisetti, and A. V. Vasilakos, “Affectively intelligent and adaptive car interfaces,” Information Sciences, vol. 180, no. 20, pp. 3817–3836, 2010.
[6] M. Marwitz and G. Stemmler, “On the status of individual response specificity,” Psychophysiology, vol. 35, no. 1, pp. 1–15, 1998.
[7] G. Stemmler, Differential psychophysiology: Persons in situations.
Springer-Verlag Berlin,, Germany, 1992.
[8] P. Ekman, Handbook of Social Psychophysiology. Wiley handbooks of psychophysiology. Oxford, England: John Wiley & Sons, 1989, ch. The Argument and
Evidence About Universals in Facial Expressions of Emotion, pp. 143–164.
[9] B. Schuller, G. Rigoll, and M. Lang, “Hidden markov model-based speech emotion recognition,” in Proceedings of the 2003 IEEE Inter- national Conference on
Acoustics, Speech, & Signal Processing, vol. 2. IEEE, 2003, pp. II–1.
[10] H. Gunes and M. Piccardi, “Bi-modal emotion recognition from ex- pressive face and body gestures,” Journal of Network and Computer Applications, vol. 30, no. 4,
pp. 1334–1345, 2007.
[11] G. Castellano, L. Kessous, and G. Caridakis, “Emotion recognition through multiple modalities: face, body gesture, speech,” in Affect and emotion in humancomputer interaction. Springer, 2008, pp. 92–103.
[12] J. Kim and E. Andre´, “Emotion recognition based on physiological changes in music listening,” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 30, no. 12, pp. 2067–2083, 2008.
[13] P. J. Lang, M. M. Bradley, B. N. Cuthbert et al., “International affective picture system (iaps): Instruction manual and affective ratings,” The center for research in
psychophysiology, University of Florida, 1999.
[14] A. Haag, S. Goronzy, P. Schaich, and J. Williams, “Emotion recognition using bio-sensors: First steps towards an automatic system,” in Affective Dialogue Systems.
Springer, 2004, pp. 36–48.
[15] G. Valenza, A. Lanata, and E. P. Scilingo, “The role of nonlinear dy- namics in affective valence and arousal recognition,” IEEE Transactions On Affective
Computing, vol. 3, no. 2, pp. 237–249, 2012.
[16] O. Alzoubi, M. S. Hussain, S. D’Mello, and R. A. Calvo, “Affective modeling from multichannel physiology: analysis of day differences,” in Affective Computing
and Intelligent Interaction. Springer, 2011, pp. 4–13.
References


















[17] S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi,
T. Pun, A. Nijholt, and I. Patras, “Deap: A database for emotion analysis; using physiological signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31,
2012.
[18] A. C. Graesser, P. Chipman, B. C. Haynes, and A. Olney, “Autotutor: An intelligent tutoring system with mixed-initiative dialogue,” IEEE Transactions on
Education, vol. 48, no. 4, pp. 612–618, 2005.
[19] G. Chanel, C. Rebetez, M. Be´trancourt, and T. Pun, “Emotion assess- ment from physiological signals for adaptation of game difficulty,” IEEE Transactions on
Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 41, no. 6, pp. 1052–1063, 2011.
[20] R. W. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence: Analysis of affective physiological state,” IEEE Transac- tions on Pattern Analysis
and Machine Intelligence, vol. 23, no. 10, pp. 1175–1191, 2001.
[21] J. Wagner, J. Kim, and E. Andre´, “From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification,”
in IEEE International Conference on Multimedia and Expo. IEEE, 2005, pp. 940–943.
[22] K. H. Kim, S. Bang, and S. Kim, “Emotion recognition system using short-term monitoring of physiological signals,” Medical and biological engineering and
computing, vol. 42, no. 3, pp. 419–427, 2004.
[23] Y. Mohammad and T. Nishida, “Using physiological signals to detect natural interactive behavior,” Applied Intelligence, vol. 33, no. 1, pp. 79–92, 2010.
[24] F. Zhou, X. Qu, M. G. Helander, and J. R. Jiao, “Affect prediction from physiological measures via visual stimuli,” International Journal of Human-Computer
Studies, vol. 69, no. 12, pp. 801–819, 2011.
[25] V. Kolodyazhniy, S. D. Kreibig, J. J. Gross, W. T. Roth, and F. H. Wilhelm, “An affective computing approach to physiological emotion specificity: Toward subjectindependent and stimulus-independent clas- sification of film-induced emotions,” Psychophysiology, vol. 48, no. 7, pp. 908–922, 2011.
[26] R. A. Calvo, I. Brown, and S. Scheding, “Effect of experimental factors on the recognition of affective mental states through physiological measures,” in AI 2009:
Advances in Artificial Intelligence. Springer, 2009, pp. 62–70.
[27] J. I. Lacey, “Psychophysiological approaches to the evaluation of psy- chotherapeutic process and outcome.” in Research in Psychotherapy, Apr, 1958, Washington,
DC; This conference, financed by a grant (M-2031) from the National Institute of Mental Health, US Public Health Service, was held under the auspices of the Division of
Clinical Psychology, American Psychological Association, with planning and programming by an Ad Hoc Committee of the Division of Clinical Psychology; Frank Auld,
Jr., Morris B. Parloff, Benjamin Pasamanick, George Saslow, Julius Seeman, and Eli A. Rubinstein, Chairman. Amer- ican Psychological Association, 1959.
[28] J. Lacey, “Somatic response patterning and stress: Some revisions of activation theory,” Psychological stress: Issues in research, pp. 14–42, 1967.
[29] M. T. Allen, A. J. Boquet, and K. S. Shelley, “Cluster analyses of cardiovascular responsivity to three laboratory stressors.” Psychosomatic Medicine, vol. 53, no. 3,
pp. 272–288, 1991.
[30] B. T. Engel, “Stimulus-response and individual-response specificity,”
Archives of General Psychiatry, vol. 2, no. 3, p. 305, 1960.
[31] G. Stemmler and J. Wacker, “Personality, emotion, and individual differences in physiological responses,” Biological psychology, vol. 84, no. 3, pp. 541–551, 2010.
[32] D. Plewczynski, S. A. Spieser, and U. Koch, “Assessing different classi- fication methods for virtual screening,” Journal of chemical information and modeling, vol.
46, no. 3, pp. 1098–1106, 2006.
Thanks You for Your Attention