Skip to main content

Towards Non-invasive Patient Monitoring Through Iris Tracking and Pain Detection

  • Conference paper
  • First Online:
XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

Part of the book series: IFMBE Proceedings ((IFMBE,volume 57))

Abstract

Patient monitoring is an important operation taking place in hospitals. It usually involves the use of dedicated invasive equipment that requires the co-operation of patients and also involves remarkable purchase and maintenance costs. In this paper we describe a feasibility study of using image analysis techniques for implementing a low-cost non-invasive patient monitoring system based on iris tracking and pain detection in image sequences captured with ordinary video cameras. Within this context iris tracking can be used for activity monitoring and also as a means for communication in cases where body movement is disabled. Automatic pain detection can be used for detecting increasing pain levels and automatically request help for the patient. As part of our preliminary investigation pain detection is achieved based on a number of texture features extracted from the shape-normalized facial regions in image sequences. Iris tracking is carried out by a method based on circular edge detection and isophote curves. The initial results of our study prove the feasibility of the approach as the basis of implementing a complete non-invasive patient monitoring system. Further validation and work in a larger sample of videos is required for further validating the proposed method.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lucey P, Cohn JF, Prkachin KM, Solomon PE, Matthews I (2011) Painful data: The UNBC-McMaster shoulder pain expression archive database. IEEE FG Int Conf, pp. 57-64

    Google Scholar 

  2. Daugman, John G (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal & Mach Intellig 15(11):1148-1161

    Google Scholar 

  3. Valenti R, Gevers T (2012) Accurate eye center location through invariant isocentric patterns. IEEE TransPatt Analysis & Mach Intellig, 34(9): 1785-1798.

    Google Scholar 

  4. Fasel B, Luettin J (2003) Automatic facial expression analysis: a survey. Patt Recogn, 36(1): 259-275

    Google Scholar 

  5. Zeng Z, Pantic M, Roisman G, Huang TS (2009) A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans PatAnal & Mach Intellig, 31(1):39-58

    Google Scholar 

  6. Bartlett M, Littlewort G, Frank M, Lainscsek C, Fasel I, Movellan J (2006) Automatic Recognition of Facial Actions in Spontaneous Expressions. Journal of multimedia, 1(6):22-35.

    Google Scholar 

  7. Lucey P, Cohn J, Matthews I, Lucey S, Howlett J, Sridharan S, Prkachin K (2010) Automatically Detecting Pain in Video Through Facial Action Units, IEEE Trans Syst Man & Cybernetics, Part B, 41(3):664-674

    Google Scholar 

  8. Ashraf A, Lucey S, Cohn J, Prkachin KM, Solomon P (2009) The Painful Face II– Pain Expression Recognition using Active Appearance Models. Image & Vision Comput. 27:12: 1788–1796

    Google Scholar 

  9. Hammal Z, Cohn JF (2012) Automatic detection of pain intensity. In Proceedings of the 14th ACM Int Conf Multimodal Interaction, pp 47-52. ACM

    Google Scholar 

  10. Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image & Vision Comput 27(6): 803-816.

    Google Scholar 

  11. Hutchinson T, White KP, Martin WN, Reichert KC, Frey LA (1989) Human-Computer interaction using eye-gaze input. IEEE SMC,19(6):1527-1534

    Google Scholar 

  12. Betke M, Mullally W (2000) Preliminary investigation of real-time monitoring of a driver in city traffic. IEEEIntelligent Vehicles Symposium, pp 563-568

    Google Scholar 

  13. Frangeskides F, Lanitis A (2008) Multi-modal contact-less human computer interaction. In Enterprise Information Systems, pp. 405-419. Springer Berlin Heidelberg

    Google Scholar 

  14. Navab A et al. (2012) Eye-Tracking as a Measure of Responsiveness to Joint Attention in Infants at Risk for Autism. Infancy 17(4):416-431

    Google Scholar 

  15. Samadani U et al. (2015) Eye tracking detects disconjugate eye movements associated with structural traumatic brain injury and concussion. J Neurotrauma 32(8):548-556

    Google Scholar 

  16. Zanelli J et al. (2005) Eye tracking in schizophrenia: Does the antisaccade task measure anything that the smooth pursuit task does not? Psychiatry Res 136(2):181-188

    Google Scholar 

  17. Richard P. Wildes (1997) Iris recognition: An emerging biometric technology. Proc IEEE 85(9):1348-1363

    Google Scholar 

  18. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. PAMI, 25(5):564-577

    Google Scholar 

  19. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides AN (2003) Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imag 22(7):902-912

    Google Scholar 

  20. Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cyber SMC 3:610-621

    Google Scholar 

  21. Weszka JS, Dyer CR, Rosenfield A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cyber SMC 6:269-285

    Google Scholar 

  22. Amadasun M, King R. (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cyber. 19(5):264-741

    Google Scholar 

  23. Wu CM, Chen YC., Hsieh K-S (1992) Texture features for classification of ultrasonic images. IEEE Trans Med Imag 11:141-152

    Google Scholar 

  24. Galloway M (1975) Texture analysis using gray level run lengths. Comp Graph & Img Process 4:172-179

    Google Scholar 

  25. Ekman P, Rosenberg EL (1997) What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford University Press

    Google Scholar 

  26. Geusebroek J, Smeulders A, van de Weijer J (2003) Fast anisotropic gauss filtering. TIP, 12(8):938-43

    Google Scholar 

  27. Jesorsky O, Kirchbergand KJ, Frischholz R (1992) Robust face detection using the Hausdorff distance. In Audio and Video Biom.Pers. Auth, pp 90-95

    Google Scholar 

  28. Froba B, Ernst A (2004) Face detection with the modified census transform. Aut Face Gest Recog, pp 91-96

    Google Scholar 

  29. Asteriadis S, Nikolaidis N, Hajdu A, Pitas I (2006) An eye detection algorithm using pixel to edge information. Int. Symp. on Control, Communication. and Signal. Processing.

    Google Scholar 

  30. Cristinacce D, Cootes T, Scott I (2004) A multi-stage approach to facial feature detection. BMVC Proc., British Machine Vision Conf, pp 1-10

    Google Scholar 

  31. Turkan M, Pardas M, Cetin A (2007) Human eye localization using edge projection. VISAPP Proc., Int. Conf. on Comp. Vision Theory & App., pp 410-415

    Google Scholar 

  32. Bai L, Shen L, Wang Y (2006) A novel eye location algorithm based on radial symmetry transform. ICPR Proc. vol. 3, IEEE Int. Conf. on Pattern Rec., pp 511-514

    Google Scholar 

  33. Campadelli P, Lanzarotti R, Lipori G (2006) Precise eye localization through a general-to-specific model definition. BMVC Proc., British Mach. Vis. Conf., pp 187-196

    Google Scholar 

  34. Hamouz M, Kittler J, Kamarainen JK, Paalanen P, Kalviainen H, Matas J (2005) Feature-based affine-invariant localization of faces. IEEE Trans Pattern Analysis & Machine Intelligence 27(9):1490-1495

    Google Scholar 

  35. Kim S, Chung ST, Jung S, Oh D, Kim J, Cho S (2007) Multi-scale gabor feature based eye localization. World Academy of Science, Engineering and Technology 21:483-487

    Google Scholar 

  36. Niu Z, Shan S, Yan S, Chen X, Gao W (2006) 2D cascaded adaboost for eye localization. ICPR Proc. vol. 2, IEEE Int. Conf. on Patt. Rec., pp 1216-1219

    Google Scholar 

  37. Asadifard M, Shanbezadeh J (2010) Automatic adaptive center of pupil detection using face detection and cdf analysis. IMECS Proc. vol. 1, Int. Multiconf. of Engin. & Comp. Scient., p 3

    Google Scholar 

  38. Timm F, Barth E (2011) Accurate eye centre localisation by means of gradients. VISAPP Proc., Int. Conf. on Comp. Vision Theory & App., pp 125-130

    Google Scholar 

  39. Kroon B, Hanjalic A, Maas SM (2008) Eye localization for face matching: is it always useful and under what conditions? CIVR Proc., Int. Conf. on Content-based Image & Video Retrieval, pp 379-388

    Google Scholar 

  40. Lanitis, A (2009) A survey of the effects of aging on bio-metric identity verification. Int. J of Biometrics 2(1):34-52

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to George Michael .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Michael, G., Tsaparellas, K., Panis, G., Loizou, C.P., Lanitis, A. (2016). Towards Non-invasive Patient Monitoring Through Iris Tracking and Pain Detection. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32703-7_71

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32701-3

  • Online ISBN: 978-3-319-32703-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics