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International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)

IJTEEE >> Volume 3 - Issue 8, August 2015 Edition

International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289

Pain Assessment Using Facial Expressions By Human And Machine

[Full Text]



Sanjay Kumar Singh, V.Rastogi, I .L . Singh, S.K.Singh



Keywords: Pain Expressions; Pain Intensity; Self-Report; Active Appearance Models (AAMs); Support Vector Machines (SVMs).



ABSTRACT: PAIN is the most common reason that patients go to visit doctors. Pain is not an easy sensation which will be simply accessed and could be directly measured. To make the right diagnosis and determine the most effective treatment plan for patients presenting with pain, systematic and precise pain assessment is required. The visual changes reflected on the face of a person in pain may be apparent for only a few seconds and occur instinctively. Tracking these changes is a time-consuming and difficult process in a clinical setting. This is often why it's motivating researchers and experts from medical, psychology and computer fields to conduct inter-disciplinary research in capturing facial expressions. The facial expressions of children's (0-2 years) in pain and in non-communicative patients need to be recognized as they are of utmost importance for proper diagnosis. The direct measurement of pain is related to the computation approach whereas indirect measurement is by observers ratings. The aim of this study is to correlate the results obtained from the observer, practitioner, and machine. The results showed that the experts often underestimated pain intensity in comparison to the observer and computational approaches used. This will cover both spheres of psychological vulnerability and resilience to pain along with the advanced techniques used in machine learning thereby improving the quality of care by increasing its accessibility to physicians.



[1] A.Huguet, J.N.Stinson and P.J.McGrath, “Measurement of self-reported pain intensity in children and Adolescents,” Journal of psychosomatic research, vol. 68, pp. 329 – 336, 2010.

[2] D. Simon, K.D.Craig, F. Gosselin, P. Belin and P.Rainville, “Recognition and discrimination of prototypical dynamic expressions of pain and emotions,” Pain, vol. 135, pp. 55–64, 2008.

[3] M.Rotteveel and R.H. Phaf, “Automatic affective evaluation does not automatically predispose for arm flexion and extension,” Emotion, vol. 4, pp.156–172, 2004.

[4] M. Gonzalez-Roldan,M. Martınez-Jauand, M.A. Munoz-Garcıa, C. Sitges and I. Cifre, “Temporal dissociation in the brain processing of pain and anger faces with different intensities of emotional expression,” Pain, vol. 152, pp. 853–859, 2011.

[5] A.M.Gonzalez-Roldan, M.A.Munoz,I. Cifre, C. Sitges and P. Montoya, “Altered psychophysiological responses to the view of others’ pain and anger faces in fibromyalgia patients,” The journal of pain, vol. 14, pp. 709-719, 2013.

[6] M.S. Bartlett, G.C. Littlewort, M.G. Frank, C. Lainscsek, I .Fasel and J. Movellan, “Recognizing facial expression: machine learning and application to spontaneous behavior”. In Computer Vision and Pattern Recognition, IEEE, vol. 2, pp.568–573, 2005.

[7] G. Littlewort, J. Whitehill, T.F. Wu, N. Butko, P. Ruvolo, J. Movellan and M. Bartlett, “The motion in emotion-A CERT based approach to the FERA emotion challenge,” In Automatic Face & Gesture Recognition and Workshops IEEE , vol. 151, pp. 897–902, 2011.

[8] C.L. Von Baeyer, L.J.Spagrud, “Systematic review of observational (behavioral) measures of pain for children and adolescents aged 3 to 18 years,” Pain, vol. 127, pp.140–150, 2007.

[9] J.N.Stinson, T. Kavanagh, J. Yamada, N. Gill and B. Stevens, “Systematic review of the psychometric properties, interpretability and feasibility of self-report pain intensity measures for use in clinical trials in children and adolescents,” Pain vol. 125, no. 1, pp.143–157, 2006.

[10] D. Tomlinson, C.L. von Baeyer, J.N. Stinson and L. Sung, “A systematic review of faces scales for the self-report of pain intensity in children,” Pediatrics, 2010.

[11] K.M. Prkachin, S. Berzins and S.R.Mercer, “Encoding and decoding of pain expressions: A judgement study,” Pain, vol. 58, pp. 253–259, 1994.

[12] H. Zhou, P. Roberts and L. Horgan, “Association between self-report pain ratings of child and parent, child and nurse and parent and nurse dyads: meta analysis,” Journal of Advanced Nursing, vol. 63, pp. 334–342, 2008.

[13] K.D. Craig, “The facial expression of pain better than a thousand words? ” APS Journal , vol. 3, pp. 153–162, 1992.

[14] W.E. Fordyce and St. Louis, “Behavioral Methods for Chronic Pain and Illness,” 1976.

[15] K.M. Prkachin and K.D. Craig, “Influencing nonverbal expressions of pain: Signal detection analyses,” Pain, vol. 21, pp. 399-409, 1985.

[16] P. Ekman and W.V. Friesen, “Investigator’s guide to the Facial Action Coding System,” Palo Alto: Consulting Psychologists Press 1978.

[17] C.E. Izard, “The Face of Emotion,” New York: Appleton-Century-Crofts 1971.

[18] C. Eccleston, S. Morley, A. Williams, “Systematic review of randomized controlled trials of psychological therapy for chronic pain in children and adolescents, with a subset meta-analysis of pain relief,” Pain, vol. 99, pp.157–65, 2002.

[19] K.M. Prkachin, “ Pain behaviour is not unitary,” Behavioral Brain Science , pp. 754-757, 1986.

[20] R Rosenthal, “Judgment Studies: Design, Analysis and Meta-analysis,” Cambridge: Cambridge University Press, 1993.

[21] M.H.Davis, “A multidimensional approach to individual differences in empathy. A multidimensional approach to individual differences in empathy” pp. 10:85,1980.

[22] M.M. Bradley and P.J. Lang, “Measuring emotion: the Self-Assessment Manikin and the Semantic Differential,” J Behav Ther Exp Psychiatry, vol. 25, pp. 49–59, 1994.

[23] K. Roelofs, M.A. Hagenaars and J. Stins, “Facing Freeze: Social Threat Induces Bodily Freeze in Humans,” Psychological Science, vol. 21, pp.1575–1581, 2010.

[24] A.C Lints-Martindale, T. Hadjistavropoulos, B. Barber and S.J.Gibso, “A psychophysical investigation of the facial action coding system as an index of pain variability among older adults with and without Alzheimer's disease,” Pain, vol. 8, pp. 678-89, 2007.

[25] K.M.Prkachin, “The consistency of facial expressions of pain: a comparison across modalities,” Pain, vol. 51, pp.297–306, 1992.

[26] K.M.Prkachin and P.E.Solomon, “The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain, ” Pain , vol. 139, pp. 267-274, 2008.

[27] P. Lucey, J.F. Cohn, J. Howlett and S.Sridharan, “Recognizing emotion with head pose variation: Identifying pain segments in video,” Systems, Man, and Cybernetics – Part B vol. 41, no. 3, pp. 664-674, 2011.

[28] S. Lucey, A. Ashraf and J. Cohn, “Investigating spontaneous facial action recognition through AAM representations of the face, in Face Recognition Book”, Kurihara K, Ed. Pro Literatur Verlag, 2007.

[29] Z. Hammal and C. Massot, “Gabor-like Image Filtering for Transient Feature Detection and Global Energy Estimation Applied to Multi-Expression Classification”. In Communications in Computer and Information Science (CCIS 229) (eds. P. Richard and J.Braz, Springer, CCIS 229, pp. 135-153, 2011.

[30] T. Cootes, D. Cooper, C. Taylor and G. Graham, “Active shape models - their training and application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38–59, 1995.

[31] A.B. Ashraf, S. Lucey, J.F.Cohn, T. Chen, K.M. Prkachin and P.E.Solomon, “The painful face: Pain expression recognition using active appearance models, ” Image and Vision Computing, vol. 27, pp.1788– 1796. 2009;

[32] I.Matthews and S.Baker, “Active appearance models”revisited, International Journal of Computer Vision, vol. 60, no. 2, pp.135–164, 2004.

[33] G. Donato, M. Bartlett, J .Hager, P. Ekman and T. Sejnowski, “Classifying facial actions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 974–989. 1999.

[34] P. Lucey, S. Lucey and J.F..Cohn, “Registration invariant representations for expression detection,” International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2010.

[35] J.C.Burges-Christopher, “A Tutorial on Support Vector Machines for Pattern Recognition, ”Data Mining and Knowledge Discovery, vol. 2, pp.121–167,1998.

[36] M.S.Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, J. Movellan, “Fully Automatic Facial Action Recognition in Spontaneous Behavior,” Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 2006.

[37] M.H. Mahoor, S. Cadavid, D.S. Messinger and J.F.Cohn,“A Framework for Automated Measurement of the Intensity of Non-Posed Facial Action Units”. 2nd IEEE Workshop on CVPR for Human Communicative Behavior analysis (CVPR4HB), Miami Beach, 2009.

[38] P. Shrout and J. Fleiss, “Intraclass correlations: uses in assessing rater reliability,” Psychological Bulletin, vol. 86, no. 2, pp. 420–428, 1979.

[39] P. Lucey, J.F. Cohn, K.M. Prkachin, P.Solomon, I. Matthews, “Painful data: The UNBC-McMaster shoulder pain expression archive database, ” Image, Vision, and Computing, vol. 30, pp.197-205, 2012.