Towards Emotion Related Feature Extraction based on Generalized Source-Independent Event Detection

Rui Santos, Joana Sousa, Carlos J. Marques, Hugo Gamboa, Hugo Silva

2012

Abstract

Emotion recognition is of major importance towards the acceptability of Human-Computer Interaction systems, and several approaches to emotion classification using features extracted from biosignals have already been developed. This analysis is, in general, performed on a signal-specific basis, and can bring a significant complexity to those systems. In this paper we propose a signal-independent approach on marking specific signal events. In this preliminary study, the developed algorithm was applied on ECG and EMG signals. Based on a morphological analysis of the signal, the algorithm allows the detection of significant events within those signals. The performance of our algorithm proved to be comparable with that achieved by signal-specific processing techniques on events detection. Since no previous knowledge or signal-specific pre-processing steps are required, the presented approach is particularly interesting for automatic feature extraction in the context of emotion recognition systems.

References

  1. Picard, R.W. and Vyzas, E. and Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23. Published by the IEEE Computer Society (2001) 1175-1191
  2. Haag, A. and Goronzy, S. and Schaich, P. and Williams, J.: Emotion recognition using bio-sensors: First steps towards an automatic system. Affective Dialogue SystemsBME-32. Springer (2004) 36-48
  3. Kim, J.: Bimodal emotion recognition using speech and physiological changes. Robust Speech Recognition and Understanding. Citeseer (2007) 265-280
  4. Kim, KH and Bang, SW and Kim, SR: Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing, Vol. 42. Springer (2004) 419-427
  5. Van Den Broek, E. L. and LisÈ, V. and Westerink, J. H. D. M. and Schut, M. H. and Tuinenbreijer, K.: Biosignals as an Advanced Man-Machine Interface. BIOSTEC International Joint Conference on Biomedical Engineering Systems and Technologies. Citeseer (2009) 15-24
  6. Kim, J. and André, E.: Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 30. Published by the IEEE Computer Society (2008) 2067-2083
  7. Heo, H. and Lee, E. C. and Park, K. R. and Kim, C. J. and Whang, M.: A realistic game system using multi-modal user interfaces Consumer Electronics, IEEE Transactions on, Vol. 56. IEEE (2010) 1364-1372
  8. Wagner, J. and André, E.: From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. 2005 IEEE International Conference on Multimedia and Expo. IEEE (2005) 940-943
  9. Rigas, G. and Katsis, C. and Ganiatsas, G. and Fotiadis, D.: A user independent, biosignal based, emotion recognition method. User Modeling 2007, Vol. 4511. Springer (2007) 314- 318
  10. Hristova, E. and Grinberg, M. and Lalev, E.: Biosignal Based Emotion Analysis of HumanAgent Interactions. Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions, Vol. 5641. Springer(2009) 63-75
  11. Matsumoto, D. and Keltner, D. and Shiota, M. N. and O'Sullivan, M. and Frank, M.: Facial expressions of emotion. Handbook of emotions. The Guilford Press (2008) 211-234
  12. Vogt, T. and André, E. and Wagner, J.: Automatic recognition of emotions from speech: a review of the literature and recommendations for practical realization. Affect and Emotion in Human-Computer Interaction, Vol. 5078. Springer (2008) 75-91
  13. Kipp, M. and Martin, J. C.: Gesture and Emotion: Can basic gestural form features discriminate emotions? ACII 2009. 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009. IEEE (2009) 1-8
  14. Canento, F. and Fred, A. and Silva, H. and Gamboa, H. and Lourenc¸o, A.: Multimodal Biosignal Sensor Data Handling for Emotion Recognition IEEE Sensors 2011. IEEE International Sensors Conference, 2011.
  15. Camm, A. J. and Malik, M. and Bigger, JT and Breithardt, G. and Cerutti, S. and Cohen, RJ and Coumel, P. and Fallen, EL and Kennedy, HL and Kleiger, RE and others: Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, Vol. 93. (1996)1043-1065
  16. PhysioBank - physiologic signal archives for biomedical research (2009) [online] Available at: http://physionet.ph.biu.ac.il/physiobank/ [Accessed 8 November 2011]
  17. Hadjileontiadis, L.J.: Biosignals and compression standards. M-Health Springer(2006) 277- 292
  18. Nakasone, A. and Prendinger, H. and Ishizuka, M.: Emotion recognition from electromyography and skin conductance. The Fifth International Workshop on Biosignal Interpretation (BSI-05), Tokyo, Japan Citeseer (2005) 219-222
  19. Marques, C. J. and Gamboa, H. and Lampe, F. and Barreiros, J. and Cabri, J.: Muscle activation thresholds before and after total knee arthoplasty - Protocol of a Randomized Comparison of Minimally Invasive vs. Standard Approach Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2011) 544-547
  20. PLUX - Wireless Biosignals, S.A. (2007) [online] Available at: http://plux.info/ [Accessed 8 November 2011]
  21. Santos, R. and Sousa, J. and San˜ udo, B. and Marques, C. J. and Gamboa, H.: Biosignals events detection - a morphological signal-independent approach. Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2012)
  22. Pan, J. and Tompkins, W. J.: A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, Vol. BME-32. IEEE (1985) 230 - 236
  23. Hodges, P. W. and Bui, B. H.: A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. Electroencephalography and Clinical Neurophysiology/Electromyography and Motor Control, Vol. 101. Elsevier (1996) 511-519
  24. Staude, G. and Flachenecker, C. and Daumer, M. and Wolf, W.: Onset detection in surface electromyographic signals: a systematic comparison of methods. EURASIP Journal on Applied Signal Processing, Vol. 2001. Hindawi Publishing Corp. (2001) 67-81
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Paper Citation


in Harvard Style

Santos R., Sousa J., J. Marques C., Gamboa H. and Silva H. (2012). Towards Emotion Related Feature Extraction based on Generalized Source-Independent Event Detection . In Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health - Volume 1: MindCare, (BIOSTEC 2012) ISBN 978-989-8425-92-8, pages 71-78. DOI: 10.5220/0003891900710078


in Bibtex Style

@conference{mindcare12,
author={Rui Santos and Joana Sousa and Carlos J. Marques and Hugo Gamboa and Hugo Silva},
title={Towards Emotion Related Feature Extraction based on Generalized Source-Independent Event Detection},
booktitle={Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health - Volume 1: MindCare, (BIOSTEC 2012)},
year={2012},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003891900710078},
isbn={978-989-8425-92-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health - Volume 1: MindCare, (BIOSTEC 2012)
TI - Towards Emotion Related Feature Extraction based on Generalized Source-Independent Event Detection
SN - 978-989-8425-92-8
AU - Santos R.
AU - Sousa J.
AU - J. Marques C.
AU - Gamboa H.
AU - Silva H.
PY - 2012
SP - 71
EP - 78
DO - 10.5220/0003891900710078