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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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