Skip to main content
SpringerLink
Log in

Prediction of pain intensity using multimedia data

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Increased medical expenditure, improper treatment, decreased productivity and above all inadequate pain, assessment is the global challenging problem, causing a substantial burden on the individual, their family, and society which often leads to loss of interest in life. The facial expression variation generally reflects clue for the occurrence of pain. This offers a vital aspect for non-verbal patients who are not in a position to rate their pain intensity level. The hypothesis proposes the developing of a multimodal machine based tool which will assess pain intensity. A framework has been designed to meet up with these specific issues which involve extracting features from the face and genes involved in pain. Patients suffering from pain require a comprehensive assessment to be conducted for proper diagnosis. The contributions of this paper are fourfold: Firstly, this paper provides an efficient approach to computational pain quantification. Secondly, it investigates the medical practitioner perception to pain along with the readings of the various tools available. Thirdly, the psychological aspect is taken into consideration to predict how pain is perceived by observers and experts (physicians). Fourthly, genes involved in pain and no pain conditions are taken and classified. Three large databases of spontaneous pain expressions are used i.e., McMaster UNBC Pain Archive database, self-prepared database and the other BioVid heat pain database of pain to verify the accuracy and robustness of the system. The methodology achieves 87% accuracy rate for classification of frames amid four levels of pain intensity. Designing intelligent computing systems with the given methodology will certainly improve the quality of life of patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. ACdC W, HTO D, Chadury Y (2000) Simple pain rating scales hide complex idiosyncratic meanings. Pain 85(3):457–463

    Article  Google Scholar 

  2. Ashraf AB, Lucey S, Cohn JF, Chen T, Ambadar Z, Prkachin KM, Solomon PE (2009) The painful face–pain expression recognition using active appearance models. Image Vis Comput 27(12):1788–1796

    Article  Google Scholar 

  3. Basbaum AI, Bautista DM, Scherrer G, Julius D (2009) Cellular and molecular mechanisms of pain. Cell 139:267–284

    Article  Google Scholar 

  4. Breivik H, Borchgrevink PC, Allen SM, Rosseland LA, Romundstad L, Hals EK, Kvarstein G, Stubhaug A (2008) Assessment of pain. Br J Anaesth 101:17–24

    Article  Google Scholar 

  5. Burckhardt CS, Jones KD, Adult measures of pain (2003) The McGill pain questionnaire (MPQ), rheumatoid arthritis pain scale (RAPS), ShortForm McGill pain questionnaire (SF-MPQ), verbal descriptive scale (VDS), visual analog scale (VAS), and west haven-Yale multidisciplinary pain inventory (WHYMPI). Arthritis Care Res 49(S5):S96–S104

    Article  Google Scholar 

  6. Caroline S, Camille C, Steve M, Olivia G, Marie DV, Steven L (2010) Assessment and detection of pain in noncommunicative severely brain-injured patients. Expert Rev Neurother 10(11):1725–1731

    Article  Google Scholar 

  7. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  8. Debono DJ, Hoeksema LJ, Hobbs RD (2013) Caring for patients with chronic pain: pearls and pitfalls. J Am Osteopath Assoc 113(8):620–627

    Article  Google Scholar 

  9. Eisenberger NI, Lieberman M (2005) Why it hurts to be left out: the neurocognitive overlap between physical and social pain. In: Williams KD. The social outcast: ostracism, social exclusion, rejection, & bullying (Sydney symposium of social psychology). Psychology Press, East Sussex, p 210

    Google Scholar 

  10. Ekman P, Friesen WV, Hager J: Facial action coding system (FACS) (2002) A technique for the measurement of facial movement. Research nexus 2002; salt Lake City, UT

  11. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. JOSA A 14(8):1724–1733

    Article  Google Scholar 

  12. Ferraz MB, Quaresma M, Aquino L, Atra E, Tugwell P, Goldsmith C (1990) Reliability of pain scales in the assessment of literate and illiterate patients with rheumatoid arthritis. J Rheumatol 17(8):1022–1024

    Google Scholar 

  13. Gea J, Munoz MA, Costa I, Ciria LF, Miranda JGV, Montoya P (2014) Viewing pain and happy faces elicited similar changes in postural body sway. PLoS One 9(8):e104381

    Article  Google Scholar 

  14. Hadjistavropoulos HD, Craig KD, Hadjistavropoulos T, Poole GD (1996) Subjective judgments of deception in pain expression: accuracy and errors. Pain 65(2):251–258

    Article  Google Scholar 

  15. Hammal Z, Cohn JF (2012) Automaticdetection of pain intensity. In: proceedings of the 14th ACM international conference on multimodal interaction. ACM, 47–52

  16. Hammal Z, Kunz M (2012) Pain monitoring: a dynamic and context-sensitive system. Pattern Recogn 45(4):1265–1280

    Article  Google Scholar 

  17. Herbert B, Andreas E, Tinne T, Gool LV (2008) Surf: speeded up robust features. Comp Vision Image Underst (CVIU) 110(3):346–359

    Article  Google Scholar 

  18. Huskisson EC (1974) Measurement of pain. Lancet 304(7889):1127–1131

    Article  Google Scholar 

  19. Jensen MP, Karoly P, Braver S (1986) The measurement of clinical pain intensity: a comparison of six methods. Pain 27(1):117–126

    Article  Google Scholar 

  20. Krishtal O (2003) The ASICs: signaling molecules? Modulators? Trends Neurosci 26(9):477–483

    Article  Google Scholar 

  21. Liu C, Wechsler H (1999) Comparative assessment of independent component analysis for face recognition. In: International conference on audio and video based biometric person authentication. Citeseer, p 22–24

  22. Lucey P, Cohn JF, Prkachin KM, Solomon PE, Matthews I (2011) Painful data: the UNBC-McMaster shoulder pain expression archive database. IEEE International Conference on Automatic Face & Gesture Recognition and Workshops. IEEE, In, pp 57–64

    Google Scholar 

  23. Lucey P, Cohn JF, Howlett J, Sridharan S (2011) Recognizing emotion with head pose variation: identifying pain segments in the video. Systems, Man, and Cybernetics – Part B 41(3):664–674

    Article  Google Scholar 

  24. Lucey P, Cohn JF, Matthews I, Lucey S, Sridharan S, Howlett J, Prkachin KM (2011b) Automatically detecting pain in video through facial action units. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(3):664–674

    Article  Google Scholar 

  25. Lucey P, Cohn JF, Prkachin KM, Solomon PE, Chew S, Matthews I (2012) Painful monitoring: automatic pain monitoring using the UNBCMcMaster shoulder pain expression archive database. Image Vis Comput 30(3):197–205

    Article  Google Scholar 

  26. McCormack HM, Horne DJL, Sheather S (1988) Clinical applications of visual analogue scales: a critical review. Psychol Med 18(04):1007–1019

    Article  Google Scholar 

  27. McKinney BA, Reif DM, Ritchie MD, Moore JH (2006) Machine learning for detecting gene-gene interactions: a review. Appl Bioinforma 5(2):77–88

    Article  Google Scholar 

  28. Prkachin KM, Solomon PE (2008) The structure, reliability and validity of pain expression: evidence from patients with shoulder pain. Pain 139(2):267–274

    Article  Google Scholar 

  29. Roelofs K, Hagenaars MA, Stins J (2010) Facing freeze: social threat induces bodily freeze in humans. Psychol Sci 21:1575–1581

    Article  Google Scholar 

  30. Simon D, Craig KD, Gosselin F, Belin P, Rainville P (2008) Recognition and discrimination of prototypical dynamic expressions of pain and emotions. Pain 135:55–64

    Article  Google Scholar 

  31. Walter S et al (2013) The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system. 2013 I.E. international conference on Cybernetics (CYBCO), Lausanne, p 128-131

  32. Wemmie JA, Price MP, Welsh MJ (2006) Acid-sensing ion channels: advances, questions and therapeutic opportunities. Trends Neurosci 29:578–586

    Article  Google Scholar 

  33. Wemmie JA, Rebecca JT, Collin JK (2013) Acid –sensing ion channels in pain and disease. Nat Rev Neurosci 14(7):461–471

    Article  Google Scholar 

  34. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1):37–52

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rajarshi School of Management and Technology, Varanasi, India

    Sanjay Kumar Singh

  2. Faculty of Engineering & Technology, Junwani, Bhilai, Durg, Chattisgarh, India

    Shrikant Tiwari

  3. Designer at the National Institute of Design, Ahmedabad, India

    Ali Imam Abidi

  4. Kamala Nehru Institute of Technology, Sultanpur, India

    Aruni Singh

Authors
  1. Sanjay Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shrikant Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Imam Abidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aruni Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay Kumar Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S.K., Tiwari, S., Abidi, A.I. et al. Prediction of pain intensity using multimedia data. Multimed Tools Appl 76, 19317–19342 (2017). https://doi.org/10.1007/s11042-017-4718-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4718-6

Keywords

Search

Navigation