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Monitoring the Impact of Stress on Facial Skin Using Affective Computing

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Predictive Analytics of Psychological Disorders in Healthcare

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 128))

Abstract

Nowadays, individuals and communities are benefiting from early recognition of stress. The American Psychological Association states that 75% of individuals are suffering moderate to severe levels of stress. Traditional stress detection methods rely on physiological signals, which are contact-based and require sensors to be in close proximity to humans. As a result, developing a reliable method of detecting stress that does not rely on physiological signaling is still a challenge. Several researchers in the field have used facial expressions and physiological signals to indicate stress levels. Stress might manifest itself in the form of facial skin conditions in the severe stage. The American Institute of Stress states that stress causes damage to the facial skin in different ways. There is, however, a gap in the field for describing stress using a combination of valence, arousal, and physical changes caused to the face. Contribution: This research is evaluating the relationship between mind (stress) and skin (facial skin) using factors such as valence, arousal, and facial skin conditions using the affective computing approach. Results: We investigated the mind-skin connection between 37 emotional test subjects (mean age: 31 ± 4 yrs.), and observed that V-A evaluation was 80.83% accurately showed promising results. The stress test group had a direct association with their facial skin, according to our findings. Moreover, 85.5% of the stress test group were suffered from high to server-level of skin conditions.

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References

  1. J.E. Driskell, E. Salas (eds.), Stress and Human Performance (Psychology Press, 2013)

    Google Scholar 

  2. B.B. Arnetz, R. Ekman, Stress in Health and Disease (Wiley-VCH, 2006), pp. 280–291

    Google Scholar 

  3. H. Selye, The stress syndrome. Am. J Nursing, 97–99 (1965)

    Google Scholar 

  4. B. Chhetri, L.M. Goyal, M. Mittal, G. Battineni, Estimating the prevalence of stress among Indian students during the COVID-19 pandemic: A cross-sectional study from India. J. Taibah Univ. Med. Sci. 16(2), 260–267 (2021)

    Google Scholar 

  5. B. Chhetri, L. Goyal, M. Mittal, S. Gurung, Consumption of licit and illicit substances leading to mental illness: a prevalence study. EAI Endorsed Trans. Pervasive Health and Technol. 6(21), e5 (2020)

    Google Scholar 

  6. N. Sharma, T. Gedeon, Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput. Methods Programs Biomed. 108(3), 1287–1301 (2012)

    Article  Google Scholar 

  7. W.G. Parrott (ed.) Emotions in Social Psychology: Essential Readings (Psychology Press, 2001)

    Google Scholar 

  8. A. Alexopoulos, G.P. Chrousos, Stress-related skin disorders. Rev. Endocr. Metab. Disord. 17(3), 295–304 (2016)

    Article  Google Scholar 

  9. O.T. Osman, A. Mufaddel, F. Almugaddam, E.F. Augusterfer, The psychiatric aspects of skin disorders. Expert. Rev. Dermatol. 6(2), 195–209 (2011)

    Article  Google Scholar 

  10. A. Chuh, W. Wang, V. Zawar, The skin and the mind. Aust. Fam. Physician 35(9), 723 (2006)

    Google Scholar 

  11. R.S. Golpanian, H.S. Kim, G. Yosipovitch, Effects of stress on itch. Clin. Ther. 42(5), 745–756 (2020)

    Article  Google Scholar 

  12. Z.F. Fathallah, Aging is a process not a disease. Basrah J. Surg. 18(2) (2012)

    Google Scholar 

  13. V. Burch, D. Penman, Mindfulness for health: a practical guide to relieving pain, reducing stress and restoring wellbeing. Hachette UK (2013)

    Google Scholar 

  14. B. Dreno, E. Bagatin, U. Blume‐Peytavi, M. Rocha, H. Gollnick, Female type of adult acne: Physiological and psychological considerations and management. JDDG: J. Der Deutschen Dermatologischen Gesellschaft 16(10), 1185–1194 (2018)

    Google Scholar 

  15. Y. Chen, J. Lyga, Brain-skin connection: stress, inflammation and skin aging. Inflamm. Allergy-Drug Targets (Formerly Current Drug Targets-Inflammation & Allergy) (Discontinued) 13(3), 177–190 (2014)

    Google Scholar 

  16. A.T. Sohaib, S. Qureshi, J. Hagelbäck, O. Hilborn, P. Jerčić, Evaluating classifiers for emotion recognition using EEG, in International Conference on Augmented Cognition (Springer, Berlin, 2013), pp. 492–501

    Google Scholar 

  17. N. Sharma, A. Dhall, T. Gedeon, R. Goecke, Thermal spatio-temporal data for stress recognition. EURASIP J. Image Video Process. 2014(1), 1–12 (2014)

    Article  Google Scholar 

  18. B.G.D.A. Madhusanka, S. Ramadass, R. Premkumar, H.M.K.K.M.B. Herath, Biofeedback method for human computer interaction to improve elder caring: eye gaze tracking, in Predictive Modelling in Biomedical Data Mining and Analysis. Biomedical Engineering (Elsevier, 2022)

    Google Scholar 

  19. B.G.D.A. Madhusanka, S. Ramadass, R. Premkumar, H.M.K.K.M.B. Herath, Concentrated gaze base interaction for decision making using human-machine interface, in Multimedia Computing Systems and Virtual Reality (CRC Press, Taylor & Francis Group, London)

    Google Scholar 

  20. B.G.D.A. Madhusanka, S. Ramadass, R. Premkumar, H.M.K.K.M.B. Herath, Attention-aware recognition of activities of daily living based on eye gaze tracking, in Internet of Things for Human-Centered Design: Application to Elderly Healthcare (Springer, Singapore, 2022)

    Google Scholar 

  21. B.H. Prasetio, H. Tamura, K. Tanno. The facial stress recognition based on multi-histogram features and convolutional neural network, in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, 2018 October), pp. 881–887

    Google Scholar 

  22. B.H. Prasetio, H. Tamura, K. Tanno, Support vector slant binary tree architecture for facial stress recognition based on gabor and HOG feature, in 2018 International Workshop on Big Data and Information Security (IWBIS) (IEEE, 2018 May), pp. 63–68

    Google Scholar 

  23. P. Ekman, E.L. Rosenberg (eds.), What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS) (Oxford University Press, New York, 1997)

    Google Scholar 

  24. M. Wegrzyn, M. Vogt, B. Kireclioglu, J. Schneider, J. Kissler, Mapping the emotional face. How individual face parts contribute to successful emotion recognition. PloS One 12(5), e0177239 (2017)

    Google Scholar 

  25. S. Anders, M. Lotze, M. Erb, W. Grodd, N. Birbaumer, Brain activity underlying emotional valence and arousal: a response-related fMRI study. Hum. Brain Mapp. 23(4), 200–209 (2004)

    Article  Google Scholar 

  26. J.A. Russell, A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)

    Article  Google Scholar 

  27. R.T. Ross, A statistics for circular scales. J. Educ. Psychol. 29, 384–389 (1938)

    Article  Google Scholar 

  28. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2000)

    Google Scholar 

  29. C. Darwin, The expression of the emotions in man and animals (1872). Portable Darwin 364–393 (1993)

    Google Scholar 

  30. P. Ekman, Cross-cultural studies of facial expression. Darwin and facial expression: a century of research in review. 169222(1) (1973)

    Google Scholar 

  31. A.J. Fridlund, in The Behavioral Ecology and Sociality of Human Faces (1992)

    Google Scholar 

  32. J.A. Russell, Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies. Psychol. Bull. 115(1), 102 (1994)

    Article  Google Scholar 

  33. P. Ekman, An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)

    Article  Google Scholar 

  34. P. Ekman, Basic emotions. Handbook of Cognition and Emotion 98(45–60), 16 (1999)

    Google Scholar 

  35. A.J. Calder, Facial emotion recognition after bilateral amygdala damage: differentially severe impairment of fear. Cogn. Neuropsychol. 13(5), 699–745 (1996)

    Article  Google Scholar 

  36. D.M. Sloan, M.E. Strauss, S.W. Quirk, M. Sajatovic, Subjective and expressive emotional responses in depression. J. Affect. Disord. 46(2), 135–141 (1997)

    Article  Google Scholar 

  37. R.W. Levenson, P. Ekman, W.V. Friesen, Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology 27(4), 363–384 (1990)

    Article  Google Scholar 

  38. C.Z. Malatesta, C. Culver, J.R. Tesman, B. Shepard, A. Fogel, M. Reimers, G. Zivin, The development of emotion expression during the first two years of life. Monogr. Soc. Res. Child Develop. i-136 (1989)

    Google Scholar 

  39. R. Matias, J.F. Cohn, Are max-specified infant facial expressions during face-to-face interaction consistent with differential emotions theory? Dev. Psychol. 29(3), 524 (1993)

    Article  Google Scholar 

  40. Z. Ambadar, J.W. Schooler, J.F. Cohn, Deciphering the enigmatic face: The importance of facial dynamics in interpreting subtle facial expressions. Psychol. Sci. 16(5), 403–410 (2005)

    Article  Google Scholar 

  41. E. Hatfield, J.T. Cacioppo, R.L. Rapson, Primitive emotional contagion (1992)

    Google Scholar 

  42. U. Hess, S. Senécal, G. Kirouac, P. Herrera, P. Philippot, R.E. Kleck, Emotional expressivity in men and women: Stereotypes and self-perceptions. Cogn. Emot. 14(5), 609–642 (2000)

    Article  Google Scholar 

  43. R.E. Jack, P.G. Schyns, The human face as a dynamic tool for social communication. Curr. Biol. 25(14), R621–R634 (2015)

    Article  Google Scholar 

  44. M. Baude, E. Hutin, J.M. Gracies, A bidimensional system of facial movement analysis conception and reliability in adults. BioMed Res. Int. (2015)

    Google Scholar 

  45. S. Schwarting, M. Schröder, E. Stennert, H.H. Goebel, Morphology of denervated human facial muscles. Orl 46(5), 248–256 (1984)

    Article  Google Scholar 

  46. C.W. Goodmurphy, W.K. Ovalle, Morphological study of two human facial muscles: orbicularis oculi and corrugator supercilii. Clin. Anatomy Off. J. Am. Assoc. Clin. Anatom. Br. Assoc. Clin. Anatom. 12(1), 1–11 (1999)

    Google Scholar 

  47. P. Ekman, Facial action coding system (1977)

    Google Scholar 

  48. P. Ekman, W.V. Friesen, Manual of the Facial Action Coding System (FACS) (Consulting Psychologists Press, Palo Alto, 1978)

    Google Scholar 

  49. C. Fabian Benitez-Quiroz, R. Srinivasan, A.M. Martinez, Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 5562–5570

    Google Scholar 

  50. W.V. Friesen, P. Ekman, EMFACS-7: Emotional facial action coding system (Unpublished manuscript) (University of California at San Francisco, 2(36), 1, 1983)

    Google Scholar 

  51. A.F. Trueba, T. Ritz, Stress, asthma, and respiratory infections: pathways involving airway immunology and microbial endocrinology. Brain Behav. Immun. 29, 11–27 (2013)

    Article  Google Scholar 

  52. M.L. Harris, D. Loxton, D.W. Sibbritt, J.E. Byles, The influence of perceived stress on the onset of arthritis in women: findings from the Australian Longitudinal Study on women’s health. Ann. Behav. Med. 46(1), 9–18 (2013)

    Article  Google Scholar 

  53. E. Zoumakis, S.N. Kalantaridou, G.P. Chrousos, The “brain–skin connection”: nerve growth factor-dependent pathways for stress-induced skin disorders. J. Mol. Med. 85(12), 1347–1349 (2007)

    Article  Google Scholar 

  54. H.C. Williams, R.P. Dellavalle, S. Garner, Acne vulgaris. The Lancet 379(9813), 361–372 (2012)

    Article  Google Scholar 

  55. W.D. James, Acne. New Engl. J. Med. 352(14), 1463–1472 (2005)

    Google Scholar 

  56. G. Yosipovitch, M. Tang, A.G. Dawn, M. Chen, C.L. Goh, Y.H. Chan, L.F. Seng, Study of psychological stress, sebum production and acne vulgaris in adolescents. Acta Derm. Venereol. 87(2), 135–139 (2007)

    Article  Google Scholar 

  57. J.K. Kiecolt-Glaser, P.T. Marucha, A.M. Mercado, W.B. Malarkey, R. Glaser, Slowing of wound healing by psychological stress. Lancet 346(8984), 1194–1196 (1995)

    Article  Google Scholar 

  58. S. Zari, D. Alrahmani, The association between stress and acne among female medical students in Jeddah, Saudi Arabia. Clin. Cosmet. Investig. Dermatol. 10, 503 (2017)

    Article  Google Scholar 

  59. M. Dahmane, P.L. St-Charles, M. Lalonde, K. Heffner, S. Foucher, Arousal and valence estimation for visual non-intrusive stress monitoring, in 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA) (IEEE, 2019 November), pp. 1–6

    Google Scholar 

  60. A. Mehrabian, Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Curr. Psychol. 14(4), 261–292 (1996)

    Article  MathSciNet  Google Scholar 

  61. Y. Huang, F. Chen, S. Lv, X. Wang, Facial expression recognition: a survey. Symmetry 11(10), 1189 (2019)

    Google Scholar 

  62. P. Sarkar, A. Etemad, Self-supervised learning for ecg-based emotion recognition, in ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2020 May), pp. 3217–3221

    Google Scholar 

  63. J. Hamm, C.G. Kohler, R.C. Gur, R. Verma, Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 200(2), 237–256 (2011)

    Article  Google Scholar 

  64. L. Loijens, O. Krips, FaceReader Methodology Note. Noldus (2021). https://www.noldus.com/

  65. P. Khorrami, T. Paine, T. Huang, Do deep neural networks learn facial action units when doing expression recognition?, in Proceedings of the IEEE International Conference on Computer Vision Workshops (2015), pp. 19–27

    Google Scholar 

  66. S.D. Craig, S. D’Mello, A. Witherspoon, A. Graesser, Emote aloud during learning with AutoTutor: applying the facial action coding system to cognitive–affective states during learning. Cogn. Emot. 22(5), 777–788 (2008)

    Article  Google Scholar 

  67. R. Baheti, S. Kinariwala, Detection and analysis of stress using machine learning techniques. Int. J. Eng. Adv. Technol. (2019)

    Google Scholar 

  68. M. Chauhan, S.V. Vora, D. Dabhi, Effective stress detection using physiological parameters, in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (IEEE, 2017 March), pp. 1–6

    Google Scholar 

  69. R. Ahuja, A. Banga, Mental stress detection in university students using machine learning algorithms. Procedia Computer Sci. 152, 349–353 (2019)

    Article  Google Scholar 

  70. M. Gupta, S. Vaikole, Recognition of human mental stress using machine learning paradigms, in Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST) (2020)

    Google Scholar 

  71. N. Raichur, N. Lonakadi, P. Mural, Detection of stress using image processing and machine learning techniques. Int. J. Eng. Technol. 9(3), 1–8 (2017)

    Article  Google Scholar 

  72. A. Chiu, S.Y. Chon, A.B. Kimball, The response of skin disease to stress: changes in the severity of acne vulgaris as affected by examination stress. Arch. Dermatol. 139(7), 897–900 (2003)

    Article  Google Scholar 

  73. I. Matthews, S. Baker, Active appearance models revisited. Int. J. Comput. Vision 60(2), 135–164 (2004)

    Article  Google Scholar 

  74. P. Carcagnì, M. Del Coco, M. Leo, C. Distante, Facial expression recognition and histograms of oriented gradients: a comprehensive study. Springerplus 4(1), 1–25 (2015)

    Article  Google Scholar 

  75. A.J. Smola, B. Schölkopf, A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  76. X.S. Yang (ed.), Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications (2020)

    Google Scholar 

  77. FER-2013, Kaggle (2020, July 19). https://www.kaggle.com/msambare/fer2013

  78. V. Goulden, S.M. Clark, W.J. Cunliffe, Post-adolescent acne: a review of clinical features. Br. J. Dermatol. 136(1), 66–70 (1997)

    Article  Google Scholar 

  79. F. Poli, B. Dreno, M. Verschoore, An epidemiological study of acne in female adults: results of a survey conducted in France. J. Eur. Acad. Dermatol. Venereol. 15(6), 541–545 (2001)

    Article  Google Scholar 

  80. J. Green, R.D. Sinclair, RESEARCH REPORT Perceptions of acne vulgaris in final year medical student written examination answers. Australas. J. Dermatol. 42(2), 98–101 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering Technology, The Open University of Sri Lanka, Nugegoda, Sri Lanka

    G. M. K. B. Karunasena

  2. Delhi Skill and Entrepreneurship University, New Delhi, India

    Mamta Mittal

  3. Faculty of Engineering, Sri Lanka Technological Campus, Padukka, Sri Lanka

    H. M. K. K. M. B. Herath

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  1. H. M. K. K. M. B. Herath
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Correspondence to H. M. K. K. M. B. Herath .

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  1. Delhi Skill and Entrepreneurship University, New Delhi, India

    Mamta Mittal

  2. Department of Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

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Herath, H.M.K.K.M.B., Karunasena, G.M.K.B., Mittal, M. (2022). Monitoring the Impact of Stress on Facial Skin Using Affective Computing. In: Mittal, M., Goyal, L.M. (eds) Predictive Analytics of Psychological Disorders in Healthcare. Lecture Notes on Data Engineering and Communications Technologies, vol 128. Springer, Singapore. https://doi.org/10.1007/978-981-19-1724-0_4

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