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A novel EDA glove based on textile-integrated electrodes for affective computing

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Abstract

This paper reports on performance evaluation of a preliminary system prototype based on a fabric glove, with integrated textile electrodes placed at the fingertips, able to acquire and process the electrodermal response (EDR) to discriminate affective states. First, textile electrodes have been characterized in terms of voltage–current characteristics and trans-surface electric impedance. Next, signal quality of EDR acquired simultaneously from textile and standard electrodes was comparatively evaluated. Finally, a dedicated experiment in which 35 subjects were enrolled, aiming at discriminating different affective states using only EDR was designed and realized. A new set of features extracted from non-linear methods were used, improving remarkably successful recognition rates. Results are, indeed, very satisfactory and promising in the field of affective computing.

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References

  1. Bradley MM, Lang PJ (2000) Affective reactions to acoustic stimuli. Psychophysiology 37(2):204–215

    Article  PubMed  CAS  Google Scholar 

  2. Brillinger DR, Rosenblatt M, Petropulu PM (1967) Computation and interpretation of kth order spectra. Spectral analysis of time series. Wiley, New York, pp 189–232

    Google Scholar 

  3. Chang TT Ng, Sun SF (2004) Blind detection of photomontage using higher order statistics. In: Proceedings of IEEE international symposium on circuits and systems (ISCAS), Vancouver, Canada

  4. Christie IC, Friedman BH (2004) Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach. Int J Psychophysiol 51(2):143–153

    Article  PubMed  Google Scholar 

  5. Chua KC, Chandran V, Acharya UR, Lim CM (2008) Cardiac state diagnosis using higher order spectra of heart rate variability. J Med Eng Technol 32(2):145–155

    Article  PubMed  CAS  Google Scholar 

  6. Damasio A (2005) Descartes error: emotion, reason, and the human brain. Putnam, USA

    Google Scholar 

  7. De Rossi D, Carpi F, Lorussi F, Mazzoldi A, Paradiso R, Scilingo EP, Tognetti A (2003) Electroactive fabrics and wearable biomonitoring devices. AUTEX Res J 3(4):180–185

    Google Scholar 

  8. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Citeseer. Wiley, New York

    Google Scholar 

  9. Eckmann JP, Kamphorst SO, Ruelle D (1987) Recurrence plots of dynamical systems. Europhys Lett 4:973

    Article  Google Scholar 

  10. Fowles DC, Christie MJ, Edelberg R, Grings WW, Lykken DT, Venables PH (1981) Publication recommendations for electrodermal measurements. Psychophysiology 18(3):232–239

    Article  PubMed  CAS  Google Scholar 

  11. Freedman LW, Scerbo AS, Dawson ME, Raine A, McClure WO, Venables PH (1994) The relationship of sweat gland count to electrodermal activity. Psychophysiology 31(2):196–200

    Article  PubMed  CAS  Google Scholar 

  12. Geddes LA (1997) Historical evolution of circuit models for the electrode-electrolyte interface. Ann Biomed Eng 25(1):1–14

    Article  PubMed  CAS  Google Scholar 

  13. Gomez P, Danuser B (2004) Affective and physiological responses to environmental noises and music. Int J Psychophysiol 53(2):91–103

    Article  PubMed  Google Scholar 

  14. Gratch J, Marsella S (2005) Evaluating a computational model of emotion. Auton Agent Multi Agent Syst 11(1):23–43

    Article  Google Scholar 

  15. Heiman GW (1995) Research methods in psychology. Houghton-Mifflin Co., Boston

    Google Scholar 

  16. Ishchenko AN, Shev’ev PP (1989) Automated complex for multiparameter analysis of the galvanic skin response signal. Biomed Eng 23(3):113–117

    Article  Google Scholar 

  17. Jain AK, Mao RPWJ (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  18. Jolliffe I (2002) Principal component analysis. Wiley Online Library

  19. Kim J, André E (2008) Emotion recognition based on physiological changes in music listening. In: Proceedings of IEEE transactions on pattern analysis and machine intelligence, pp 2067–2083

  20. Koelstra S, Yazdani A, Soleymani M, Mühl C, Lee JS, Nijholt A, Pun T, Ebrahimi T, Patras I (2010) Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. Brain Inf 6334:89–100

    Article  Google Scholar 

  21. Lang P, Bradley M, Cuthbert B (2005) International affective picture system IAPS: Digitized photographs, instruction manual and affective ratings. Technical report A-6. University of Florida

  22. Lang PJ, Greenwald MK, Bradley MM, Hamm AO (1993) Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3):261–273

    Article  PubMed  CAS  Google Scholar 

  23. Marwan N, Carmen Romano M, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438(5–6):237–329

    Article  Google Scholar 

  24. McCurdy HG (1950) Consciousness and the galvanometer. Psychol Rev 57(6):322–327

    Article  PubMed  CAS  Google Scholar 

  25. Mendel JM (1991) Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proc IEEE 79:278–305

    Article  Google Scholar 

  26. Nasoz F, Alvarez K, Lisetti CL, Finkelstein N (2003) Emotion recognition from physiological signals for presence technologies. Int J Cognit Technol Work-Special Issue Presence 6(1)

  27. Nikias CL (1993) Higher-order spectral analysis: a nonlinear signal processing framework. PTR Prentice-Hall Inc., USA

    Google Scholar 

  28. Onaral B, Schwan HP (1982) Linear and nonlinear properties of platinum electrode polarisation. Part 1: frequency dependence at very low frequencies. Med Biol Eng Compu 20(3):299–306

    Article  CAS  Google Scholar 

  29. Peng CK, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos Interdiscip J Nonlin Sci 5(1):82

    Article  CAS  Google Scholar 

  30. Picard RW, Vyzas E, Healey J (2001) Toward machine emotional intelligence: Analysis of affective physiological state. In: Proceedings of IEEE transactions on pattern analysis and machine intelligence 1175–1191

  31. Pollak V (1974) Computation of the impedance characteristic of metal electrodes for biological investigations. Med Biol Eng Compu 12(4):460–464

    Article  CAS  Google Scholar 

  32. Posner J, Russell JA, Peterson BS (2005) The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev Psychopathol 17(03):715–734

    Article  PubMed  Google Scholar 

  33. Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161–1178

    Article  Google Scholar 

  34. Sabatinelli D, Lang PJ, Keil A, Bradley MM (2007) Emotional perception: correlation of functional MRI and event-related potentials. Cereb Cortex 17(5):1085

    Article  PubMed  Google Scholar 

  35. Schinkel S, Dimigen O, Marwan N (2008) Selection of recurrence threshold for signal detection. Eur Phys J Special Topics 164(1):45–53

    Article  Google Scholar 

  36. Scilingo EP, Gemignani A, Paradiso R, Taccini N, Ghelarducci B, De Rossi D (2005) Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables. IEEE Trans Inf Technol Biomed 9(3):345–352

    Article  PubMed  Google Scholar 

  37. Venables PH, Christie MJ (1980) Electrodermal activity. In: Martins I, Venables PH (eds) Techniques in psychophysiology. Wiley, New York, pp 3–67

    Google Scholar 

  38. Wagner J, Kim J, André E (2005) From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In: Proceedings of the IEEE international conference on multimedia and expo, pp 940–943

  39. Winton WM, Wutnam LE, Krauss RM (1984) Facial and autonomic manifestations of the dimensional structure of emotion. J Exp Soc Psychol 20(3):195–216

    Article  Google Scholar 

  40. Zbilut JP, Webber Jr CL (2006) Recurrence quantification analysis. Wiley Online Library

Download references

Acknowledgments

This research is partially supported by the EU Commission under contracts FP7-ICT-247777 PSYCHE and FP7-ICT-258749 CEEDs.

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Authors and Affiliations

  1. Faculty of Engineering, Interdepartmental Research Centre “E. Piaggio”, University of Pisa, Via Diotisalvi, 2, 56126, Pisa, Italy

    Antonio Lanatà, Gaetano Valenza & Enzo Pasquale Scilingo

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  1. Antonio Lanatà
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  2. Gaetano Valenza
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  3. Enzo Pasquale Scilingo
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Correspondence to Enzo Pasquale Scilingo.

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Lanatà, A., Valenza, G. & Scilingo, E.P. A novel EDA glove based on textile-integrated electrodes for affective computing. Med Biol Eng Comput 50, 1163–1172 (2012). https://doi.org/10.1007/s11517-012-0921-9

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  • DOI: https://doi.org/10.1007/s11517-012-0921-9

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