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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 427826, 12 pages
http://dx.doi.org/10.1155/2014/427826
Research Article

Objectifying Facial Expressivity Assessment of Parkinson’s Patients: Preliminary Study

1Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
2Shaanxi Provincial Key Lab on Speech and Image Information Processing, Northwestern Polytechnical University, Xi’an, China
3Department of Physical Therapy, Vrije Universiteit Brussel, 1050 Brussels, Belgium
4Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, 1050 Brussels, Belgium

Received 9 June 2014; Accepted 22 September 2014; Published 13 November 2014

Academic Editor: Justin Dauwels

Copyright © 2014 Peng Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Katsikitis and I. Pilowsky, “A study of facial expression in Parkinson's disease using a novel microcomputer-based method,” Journal of Neurology Neurosurgery and Psychiatry, vol. 51, no. 3, pp. 362–366, 1988. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Simons, H. Ellgring, and M. C. Smith Pasqualini, “Disturbance of spontaneous and posed facial expressions in Parkinson's disease,” Cognition and Emotion, vol. 17, no. 5, pp. 759–778, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. D. H. Jacobs, J. Shuren, D. Bowers, and K. M. Heilman, “Emotional facial imagery, perception, and expression in Parkinson's disease,” Neurology, vol. 45, no. 9, pp. 1696–1702, 1995. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Tickle-Degnen and K. D. Lyons, “Practitioners' impressions of patients with Parkinson's disease: the social ecology of the expressive mask,” Social Science & Medicine, vol. 58, no. 3, pp. 603–614, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. J. J. van Hilten, A. D. van der Zwan, A. H. Zwinderman, and R. A. C. Roos, “Rating impairment and disability in Parkinson's disease: evaluation of the unified Parkinson's disease rating scale,” Movement Disorders, vol. 9, no. 1, pp. 84–88, 1994. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Bowers, K. Miller, W. Bosch et al., “Faces of emotion in Parkinsons disease: micro-expressivity and bradykinesia during voluntary facial expressions,” Journal of the International Neuropsychological Society, vol. 12, no. 6, pp. 765–773, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Ekman and W. Friesen, Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press, Palo Alto, Calif, USA, 1978.
  8. P. Wu, D. Jiang, and H. Sahli, “Physiological signal processing for emotional feature extraction,” in Proceedings of the International Conference on Physiological Computing Systems, pp. 40–47, 2014.
  9. L. Tickle-Degnen, The Interpersonal Communication Rating Protocol: A Manual for Measuring Individual Expressive Behavior, 2010.
  10. G. Sandbach, S. Zafeiriou, M. Pantic, and L. Yin, “Static and dynamic 3D facial expression recognition: a comprehensive survey,” Image and Vision Computing, vol. 30, no. 10, pp. 683–697, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. I. Gonzalez, H. Sahli, V. Enescu, and W. Verhelst, “Context-independent facial action unit recognition using shape and gabor phase information,” in Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction (ACII '11), vol. 1, pp. 548–557, Springer, Memphis, Tenn, USA, October 2011.
  12. J. C. Platt, “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,” in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Schoelkopf, and D. Schuurmans, Eds., pp. 61–74, The MIT Press, 1999. View at Google Scholar
  13. J. J. Gross and R. W. Levenson, “Emotion elicitation using films,” Cognition and Emotion, vol. 9, no. 1, pp. 87–108, 1995. View at Publisher · View at Google Scholar
  14. D. Hagemann, E. Naumann, S. Maier, G. Becker, A. Lürken, and D. Bartussek, “The assessment of affective reactivity using films: validity, reliability and sex differences,” Personality and Individual Differences, vol. 26, no. 4, pp. 627–639, 1999. View at Publisher · View at Google Scholar · View at Scopus
  15. C. L. Lisetti and F. Nasoz, “Using noninvasive wearable computers to recognize human emotions from physiological signals,” EURASIP Journal on Advances in Signal Processing, vol. 2004, Article ID 929414, pp. 1672–1687, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Hewig, D. Hagemann, J. Seifert, M. Gollwitzer, E. Naumann, and D. Bartussek, “A revised film set for the induction of basic emotions,” Cognition and Emotion, vol. 19, no. 7, pp. 1095–1109, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. J. H. Westerink, E. L. van den Broek, M. H. Schut, J. van Herk, and K. Tuinenbreijer, “Probing experience,” in Assessment of User Emotions and Behaviour to Development of Products, T. J. O. W. F. P. Joyce, H. D. M. Westerink, M. Ouwerkerk, and B. de Ruyter, Eds., Springer, New York, NY, USA, 2008. View at Google Scholar
  18. A. Schaefer, F. Nils, P. Philippot, and X. Sanchez, “Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers,” Cognition and Emotion, vol. 24, no. 7, pp. 1153–1172, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Verbraeck, Objectifying human facial expressions for clinical applications [M.S. thesis], Vrije Universiteit, Brussel, Belgium, 2012.
  20. O. Alzoubi, Automatic affect detection from physiological signals: practical issues [Ph.D. thesis], The University of Sydney, New South Wales, Australia, 2012.
  21. S. D. Kreibig, “Autonomic nervous system activity in emotion: a review,” Biological Psychology, vol. 84, no. 3, pp. 394–421, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. F. D. Farfán, J. C. Politti, and C. J. Felice, “Evaluation of emg processing techniques using information theory,” BioMedical Engineering Online, vol. 9, article 72, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. N. Ambady, F. J. Bernieri, and J. A. Richeson, “Toward a histology of social behavior: judgmental accuracy from thin slices of the behavioral stream,” Advances in Experimental Social Psychology, vol. 32, pp. 201–271, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. N. Ambady and R. Rosenthal, “Thin slices of expressive behavior as predictors of interpersonal consequences: a meta-analysis,” Psychological Bulletin, vol. 111, no. 2, pp. 256–274, 1992. View at Publisher · View at Google Scholar · View at Scopus
  25. K. D. Lyons and L. Tickle-Degnen, “Reliability and validity of a videotape method to describe expressive behavior in persons with Parkinson's disease,” The American Journal of Occupational Therapy, vol. 59, no. 1, pp. 41–49, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. N. A. Murphy, “Using thin slices for behavioral coding,” Journal of Nonverbal Behavior, vol. 29, no. 4, pp. 235–246, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. A. O. Andrade, S. Nasuto, P. Kyberd, C. M. Sweeney-Reed, and F. R. van Kanijn, “EMG signal filtering based on empirical mode decomposition,” Biomedical Signal Processing and Control, vol. 1, no. 1, pp. 44–55, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Blanco-Velasco, B. Weng, and K. E. Barner, “ECG signal denoising and baseline wander correction based on the empirical mode decomposition,” Computers in Biology and Medicine, vol. 38, no. 1, pp. 1–13, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. A. O. Boudraa, J. C. Cexus, and Z. Saidi, “Emd-based signal noise reduction,” Signal Processing, vol. 1, pp. 33–37, 2005. View at Google Scholar
  30. T. Jing-tian, Z. Qing, T. Yan, L. Bin, and Z. Xiao-kai, “Hilbert-Huang transform for ECG de-noising,” in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 664–667, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Karagiannis and P. Constantinou, “Noise components identification in biomedical signals based on empirical mode decomposition,” in Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine (ITAB '09), pp. 1–4, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Kopsinis and S. McLaughlin, “Development of EMD-based denoising methods inspired by wavelet thresholding,” IEEE Transactions on Signal Processing, vol. 57, no. 4, pp. 1351–1362, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. F. Agrafioti, D. Hatzinakos, and A. K. Anderson, “ECG pattern analysis for emotion detection,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 102–115, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  35. B.-U. Köhler, C. Hennig, and R. Orglmeister, “The principles of software QRS detection,” IEEE Engineering in Medicine and Biology Magazine, vol. 21, no. 1, pp. 42–57, 2002. View at Publisher · View at Google Scholar · View at Scopus
  36. V. Guralnik and J. Srivastava, “Event detection from time series data,” in Knowledge Discovery and Data Mining, pp. 33–42, 1999. View at Google Scholar
  37. M. Hamedi, S.-H. Salleh, and T. T. Swee, “Surface electromyography-based facial expression recognition in Bi-polar configuration,” Journal of Computer Science, vol. 7, no. 9, pp. 1407–1415, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. Y. Hou, H. Sahli, R. Ilse, Y. Zhang, and R. Zhao, “Robust shape-based head tracking,” in Advanced Concepts for Intelligent Vision Systems, vol. 4678 of Lecture Notes in Computer Science, pp. 340–351, Springer, 2007. View at Publisher · View at Google Scholar
  39. T. Kanade, J. F. Cohn, and Y. Tian, “Comprehensive database for facial expression analysis,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53, Grenoble, France, 2000. View at Publisher · View at Google Scholar
  40. M. S. Bartlett, G. C. Littlewort, M. G. Frank, C. Lainscsek, I. R. Fasel, and J. R. Movellan, “Automatic recognition of facial actions in spontaneous expressions,” Journal of Multimedia, vol. 1, no. 6, pp. 22–35, 2006. View at Google Scholar · View at Scopus
  41. A. Savran, B. Sankur, and M. Taha Bilge, “Regression-based intensity estimation of facial action units,” Image and Vision Computing, vol. 30, no. 10, pp. 774–784, 2012. View at Publisher · View at Google Scholar · View at Scopus
  42. Anvil, the video annotation tool, http://www.anvil-software.org/.
  43. S. Vicente, J. Péron, I. Biseul et al., “Subjective emotional experience at different stages of Parkinson's disease,” Journal of the Neurological Sciences, vol. 310, no. 1-2, pp. 241–247, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. A. van Boxtel, “Facial emg as a tool for inferring affective states,” in Proceedings of Measuring Behavior 2010, A. J. Spink, F. Grieco, O. Krips, L. Loijens, L. Noldus, and P. Zimmeran, Eds., pp. 104–108, Noldus Information Technology, Wageningen, The Netherlands, 2010. View at Google Scholar
  45. C.-N. Huang, C.-H. Chen, and H.-Y. Chung, “The review of applications and measurements in facial electromyography,” Journal of Medical and Biological Engineering, vol. 25, no. 1, pp. 15–20, 2005. View at Google Scholar · View at Scopus
  46. R. W. Levenson, P. Ekman, K. Heider, and W. V. Friesen, “Emotion and autonomic nervous system activity in the minangkabau of west sumatra,” Journal of Personality and Social Psychology, vol. 62, no. 6, pp. 972–988, 1992. View at Publisher · View at Google Scholar · View at Scopus
  47. S. Rohrmann and H. Hopp, “Cardiovascular indicators of disgust,” International Journal of Psychophysiology, vol. 68, no. 3, pp. 201–208, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. O. Alaoui-Ismaïli, O. Robin, H. Rada, A. Dittmar, and E. Vernet-Maury, “Basic emotions evoked by odorants: comparison between autonomic responses and self-evaluation,” Physiology and Behavior, vol. 62, no. 4, pp. 713–720, 1997. View at Publisher · View at Google Scholar · View at Scopus
  49. J. Gruber, S. L. Johnson, C. Oveis, and D. Keltner, “Risk for mania and positive emotional responding: too much of a good thing?” Emotion, vol. 8, no. 1, pp. 23–33, 2008. View at Publisher · View at Google Scholar · View at Scopus
  50. W. Gaebel and W. Wölwer, “Facial expressivity in the course of schizophrenia and depression,” European Archives of Psychiatry and Clinical Neuroscience, vol. 254, no. 5, pp. 335–342, 2004. View at Publisher · View at Google Scholar · View at Scopus
  51. P. Ekman, W. Friesen, and J. Hager, Facial Action Coding System: The Manual, 2002.