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Emotion detection for wheelchair navigation enhancement

Published online by Cambridge University Press:  15 August 2014

Hachem A. Lamti ,
Mohamed Moncef Ben Khelifa ,
Adel M. Alimi  and
Philippe Gorce
Hachem A. Lamti*
Affiliation:
Research Group on Intelligent Machine (REGIM) Laboratory, National School of Engineers in Sfax, Sfax, Tunisia
Mohamed Moncef Ben Khelifa
Affiliation:
Ingénierie des Handicaps et de la bio-modélisation (HANDIBIO) Laboratory, South University, Toulon-Var, France
Adel M. Alimi
Affiliation:
Research Group on Intelligent Machine (REGIM) Laboratory, National School of Engineers in Sfax, Sfax, Tunisia
Philippe Gorce
Affiliation:
Ingénierie des Handicaps et de la bio-modélisation (HANDIBIO) Laboratory, South University, Toulon-Var, France
*
*Corresponding author. E-mail: [email protected]
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Summary

The goal of this study is to investigate the use of emotion as a braking system for wheelchair navigation. In the first part emotion is detected based on ElectroEncephalography (EEG) technology and emotion induction experiments. Using different techniques for features extraction (Welch and Wavelets), selection (Principal Component Analysis (PCA) and Genetic Algorithm (GA)) and classification (Support Vector Machine (SVM), Multi Layer Perceptron (MLP) and Linear Discriminate Analysis (LDA)), the best combination was assigned to Wavelets-GA-MLP. In the second part, in order to validate the impact of emotion as velocity modulator, a comparison between emotion-based and non emotion-based wheelchair navigation scenarios in a simulated environment was conducted. The assessment was based on four parameters: obstacles hit, navigation path, execution time and outbound points of gaze (POG). While the first two emotion introductions showed better results, this was not the case for the third. These findings can be utilized in order to prescribe a suitable wheelchair according to the subject pathology.

Keywords

EmotionWheelchair navigationEEGUser performance
Type
Articles
Information
Robotica , Volume 34 , Issue 6 , June 2016 , pp. 1209 - 1226
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Powell, D. and O'Malley, M. K., “The task-dependent efficacy of shared-control haptic guidance paradigms,” IEEE Trans. Haptics 5 (3), 208–219 (2012).Google Scholar
2.Vander Poorten, E. B., Demeester, E., Hüntemann, A., Reekmans, E., Philips, J. and De Schutter, J., “Backwards Maneuvering Powered Wheelchairs with Haptic Guidance,” Proceedings of the 2012 International Conference on Haptics: Perception, Devices, Mobility, and Communication Volume Part I, EuroHaptics'12, Berlin, Heidelberg, (2012) pp. 419–431.Google Scholar
3.Urdiales, C., Perez, E. J., Peinado, G., Fdez-Carmona, M., Peula, J. M., Annicchiarico, R., Sandoval, F. and Caltagirone, C., “On the construction of a skill-based wheelchair navigation profile,” IEEE Trans. Neural Syst. Rehabil. Eng. 21 (6), 917–927 (Nov. 2013).Google Scholar
4.Ren, M. and Karimi, H. A., “A fuzzy logic map matching for wheelchair navigation,” GPS Solut. 16 (3), 273–282 (2012).Google Scholar
5.Lamti, H. A., Ben Khelifa, M. M., Gorce, P. and Alimi, A. M., “A brain and gaze-controlled wheelchair,” Comput. Methods Biomech. Biomed. Eng. 16 (1), 128–129 (2013).Google Scholar
6.Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G. and Vaughan, T., “Brain-computer interfaces for communication and control,” Clin. Neurophysiol. 113 (6), 767–791 (2002).Google Scholar
7.Steinier, P., HANDIMOBILITY, (Date: 1 Jul. 2009, Date of access: 15 May 2010), <http://www.handimobility.org/blog/?p=4953>>Google Scholar
8.Crowley, K., Sliney, A., Pitt, I. and Murphy, D., “Evaluating a Braincomputer Interface to Categorise Human Emotional Response,” Proceedings of the 2010 10th IEEE International Conference on Advanced Learning Technologies, ICALT '10, Washington, DC, USA, (2010) pp. 276–278.Google Scholar
9.Haapalainen, E., Kim, S. J., Forlizzi, J. F. and Dey, A. K., “Psychophysiological Measures for Assessing Cognitive Load,” Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Ubicomp '10, New York, NY, USA, (2010) pp. 301–310.Google Scholar
10.Gani, C., Birbaumer, N. and Strehl, U., “Long term effects after feedback of slow coritcal potentials and of theta-beta-amplitudes in children with attention-deficit/hyperactivity disorder,” Int. J. Bioelectromagnetism 10 (4), 209–232 (2008).Google Scholar
11.Arnold, M. B., Emotion and Personality, vol. 1, (New York: Columbia University Press, 1960) pp. 11–13.Google Scholar
12.Ekman, P., Friesen, W. V. and Ellsworth, P., Emotion in the Human Face (Oxford: Oxford University Press, 1972).Google Scholar
13.Russell, J. A., “A circumplex model of affect,” J. Pers. Soc. Psychol. 39 (6), 1161–1178 (Dec. 1980).CrossRefGoogle Scholar
14.Schneirla, T. C., “An Evolutionary and Developmental Theory of Biphasic Processes Underlying Approach and Withdrawal,” Nebraska Symposium on Motivation, Lincoln, (1959) pp. 1–42.Google Scholar
15.Hécaen, H., ≪Les Gauchers: étude Neurophysiologique⋙, vol. 39, (Paris: Presses Universitatires de France, 1986) pp. 128–129.Google Scholar
16.Condry, J. and Condry, S., “Sex differences: a study in the eye of the beholder,” Child Dev. 47, 812–819 (1976).Google Scholar
17.Garcia Molina, G., Tsoneva, T. and Nijholt, A., “Emotional Brain-Computer Interfaces,” Proceedings 3rd International Conference on Affective Computing and Intelligent Interaction–-ACII 2009, Amsterdam (10–12 Sep. 2009) pp. 138–146.Google Scholar
18.Feldman Barrett, L. and Russell, J. A., “Independence and bipolarity in the structure of current affect,” J. Personality Soc. Psychol. 74 (4), 967–984 (1998).CrossRefGoogle Scholar
19.De Jong, K., “Learning with genetic algorithms: an overview,” Mach. Learn. 3 (2–3), 121–138 (1988).Google Scholar
20.Friedman, J. H. K., “On bias, variance, 0/1-loss, and the curse-of-dimensionality,” Data Min. Knowl. Discovery 1 (1), 55–77 (1997).Google Scholar
21.Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A. and Patras, I., “DEAP: a database for emotion analysis; using physiological signals,” IEEE Trans. Affective Comput. 3 (1), 18–31 (Mar. 2012).Google Scholar
22.Queteschiner, D., Reality Factory, (Date: 13 Oct. 2007, Date of access: 10 Mar. 2012), <http://www.realityfactory.info/cms/tutorials.html>..>Google Scholar
23.Arai, K. and Mardiyanto, R., “Method for psychological status estimation by gaze location monitoring using eye-based human-computer interaction,” Int. J. Adv. Comput. Sci. Appl. 4 (3), 199–206 (2013).Google Scholar
24.Sharma, V., Simpson, R., LoPresti, E. and Schmeler, M., “Evaluation of semiautonomous navigation assistance system for power wheelchairs with blindfolded nondisabled individuals,” J. Rehabil. Res. Dev. 47 (9), 877–890 (2010).Google Scholar
25.Chevance, J.-P., ≪Existence et Infirmité Motrice Cérébrale⋙, (AFP Ecoute Infos, Dec. 2007) pp. 1–11.Google Scholar
26.Lang, P., Bradley, M. and Cuthbert, B., International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual, (University of Florida, USA, Tech. Rep. A-8, 2008).Google Scholar
27.Knyazev, G. G., “EEG correlates of personality types,” Netherlands J. Psychol. 62 (2), 78–87 (Dec. 2006).Google Scholar