Emotion Recognition using cvxEDA-Based Features

Authors

  • H. Ferdinando Health and Wellness Measurement Group, Optoelectronics and Measurement Technique Unit, University of Oulu, Finland. Department of Electrical Engineering, Petra Christian University, Indonesia. hany.ferdinando@oulu.fi
  • E. Alasaarela Health and Wellness Measurement Group, Optoelectronics and Measurement Technique Unit, University of Oulu, Finland.

Keywords:

cvxEDA, EDA, Emotion Recognition, MAHNOB,

Abstract

The MAHNOB-HCI database provides baselines for several modalities but not all. Up to now, there are no such baselines using EDA signal for valence and arousal recognitions. Because EDA is one of the important signals in affect recognition, it is necessary to have baseline accuracy using this signal. Applying cvxEDA, EDA tool analysis based on convex optimization, to GSR signals resulted phasic, tonic, and sudomotor neuron activity (SMNA) phasic driver. There were two sets of features extracted, i.e. features from stimulated stage only and ratio of features from stimulated to relaxation stages in addition to the former set. Using kNN to solve the 3-class problem, the best accuracies under subject-dependent scenario were 74.6 ± 3.8 and 77.3 ± 3.6 for valence and arousal respectively while subject-independent scenario resulted in 75.5 ± 7.7 and 77.8 ± 8.0 for valence and arousal correspondingly. Validation using LOO gave 75.2% and 77.7% for valence and arousal respectively. cvxEDA method looked promising to extract features from EDA as the results were even better than the best results in the original database baseline. Some future works are using other feature extraction method, enhancing the accuracies by employing supervised dimensionality reduction and using other classifiers.

References

M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A Multimodal Database for Affect Recognition and Implicit Tagging,” IEEE Trans. Affect. Comput., vol. 3, no. 1, pp. 42–55, Jan. 2012.

H. Ferdinando, L. Ye, T. Seppänen, and E. Alasaarela, “Emotion Recognition by Heart Rate Variability,” Aust. J. Basic Appl. Sci. Aust. J. Basic Appl. Sci, vol. 8, no. 814, pp. 50–55, 2014.

H. Ferdinando, T. Seppanen, and E. Alasaarela, “Comparing features from ECG pattern and HRV analysis for emotion recognition system,” in 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016, pp. 1–6.

H. Ferdinando, T. Seppänen, and E. Alasaarela, “Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction,” in Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 2017, pp. 112–118.

S. D. Kreibig, “Autonomic nervous system activity in emotion: a review.,” Biol. Psychol., vol. 84, no. 3, pp. 394–421, Jul. 2010.

A. Lanata, G. Valenza, and E. P. Scilingo, “A novel EDA glove based on textile-integrated electrodes for affective computing,” Med. Biol. Eng. Comput., vol. 50, no. 11, pp. 1163–1172, 2012.

P. Ren, A. Barreto, Y. Gao, and M. Adjouadi, “Comparison of the use of pupil diameter and galvanic skin response signals for affective assessment of computer users,” Biomed. Sci. Instrum., vol. 48, pp. 345– 350, 2012.

Z. Yang and G. Liu, “Emotion Recognition Based on Nonlinear Features of Skin Conductance Response,” J. Inf. Comput. Sci., vol. 10, no. 12, pp. 3877–3887, Aug. 2013.

A. Greco, G. Valenza, and E. P. Scilingo, Advances in electrodermal activity processing with applications for mental health : from heuristic methods to convex optimization. Cham, Switzerland: Springer International Publishing AG, 2016.

M. Benedek and C. Kaernbach, “A continuous measure of phasic electrodermal activity,” J. Neurosci. Methods, vol. 190, no. 1, pp. 80– 91, 2010.

M. Benedek and C. Kaernbach, “Decomposition of skin conductance data by means of nonnegative deconvolution,” Psychophysiology, vol. 47, no. 4, pp. 647–658, Mar. 2010.

S. Taylor, N. Jaques, W. Chen, S. Fedor, A. Sano, and R. W. Picard, “Automatic Identification of Artifacts in Electrodermal Activity Data,” in IEEE Engineering and Medicine in Biology Society, 2015, pp. 1934– 1937.

A. Greco, G. Valenza, A. Lanata, E. Scilingo, and L. Citi, “cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing,” IEEE Trans. Biomed. Eng., vol. 63, no. 4, pp. 797–804, 2016.

H. Gunes and H. Hung, “Is automatic facial expression recognition of emotions coming to a dead end? The rise of the new kids on the block,” Image Vis. Comput., vol. 55, pp. 6–8, 2016.

P. Venables and M. Christie, “Electrodermal activity,” in Techniques in psychophysiology, I. Martins and P. Venables, Eds. New York: John Wiley & Sons Inc, 1980, pp. 3–67.

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Published

2018-05-31

How to Cite

Ferdinando, H., & Alasaarela, E. (2018). Emotion Recognition using cvxEDA-Based Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 19–23. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4186

Issue

Vol. 10 No. 2-3: Technology Innovation in Electrical, Electronics and Information Technology

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Articles

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