A brain and gaze-controlled wheelchair

Lamti, Hachem A.; Khelifa, Mohamed Moncef Ben; Gorce, Philippe; Alimi, Adel M.

doi:10.1080/10255842.2013.815940

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…They also proposed an alternative to deal with difficulties faced by GPS during navigation, such as poor satellite availability, by introducing a fuzzy logic-based algorithm to perform matching wheelchair movements on sidewalks. In our pilot study, 5 a new approach based on combining the user's gaze and his mental activities was introduced in order to assess wheelchair navigation performance in comparison with a standard gaze-based navigation. The results showed that the system performance was better using the combined modalities.…”

Section: Introductionmentioning

confidence: 99%

Emotion detection for wheelchair navigation enhancement

Lamti

Khelifa²,

Alimi

et al. 2014

Robotica

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SUMMARYThe 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.

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Section: Introductionmentioning

confidence: 99%

Emotion detection for wheelchair navigation enhancement

Lamti

Khelifa²,

Alimi

et al. 2014

Robotica

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“…They found that the hybrid system could improve performance relative to SSVEP, but it was not the case for P300 BCI. Other efforts matched between brain and other modalities such as muscular activities, 22 or even visual activity 23 and the results concluded are encouraging. However, BCI suffer from some major drawbacks which can influence its acceptability among disabled groups: In fact, due to its emergence, EEG technology did not prove its reliability to fully control a powered wheelchair especially that the number of commands which could be distinguished is very limited.…”

Section: Introductionmentioning

confidence: 78%

“…This fact is also justified by the simultaneous use of gaze and SSVEP: Eyes movements are very rapid and can provide a very reactive system with satisfying execution time but biased performance. 23 The use of SSVEP to validate the selected command increases the performance at the expense of the rapidity. This trade-off is very important because in wheelchair navigation context security is granted with the highest priority.…”

Section: Systems Performances Comparisonmentioning

confidence: 99%

“…17,18 As mentioned by ref. [19], SSVEP can be elicited by three frequency ranges: low (4-12 Hz), medium (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and high (>30 Hz). While the first two are not recommended as they could cause epileptic seizures and visual fatigue, the generated SSVEP from the higher frequencies tend to be very weak.…”

Section: Introductionmentioning

confidence: 99%

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Cerebral and gaze data fusion for wheelchair navigation enhancement: case of distracted users

Lamti¹,

Khelifa²,

Hugel³

2018

Robotica

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SUMMARYThe goal of this paper is to present a new hybrid system based on the fusion of gaze data and Steady State Visual Evoked Potentials (SSVEP) not only to command a powered wheelchair, but also to account for users distraction levels (concentrated or distracted). For this purpose, a multi-layer perception neural network was set up in order to combine relevant gazing and blinking features from gaze sequence and brainwave features from occipital and parietal brain regions. The motivation behind this work is the shortages raised from the individual use of gaze-based and SSVEP-based wheelchair command techniques. The proposed framework is based on three main modules: a gaze module to select command and activate the flashing stimuli. An SSVEP module to validate the selected command. In parallel, a distraction level module estimates the intention of the user by mean of behavioral entropy and validates/inhibits the command accordingly. An experimental protocol was set up and the prototype was tested on five paraplegic subjects and compared with standard SSVEP and gaze-based systems. The results showed that the new framework performed better than conventional gaze-based and SSVEP-based systems. Navigation performance was assessed based on navigation time and obstacles collisions.

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“…The SNP system recognizes four different commands, hard sip, soft sip, hard puff, and soft puff. In [13, 14], the patient controls the wheelchair by tracking the eyes for blinks and estimating gaze direction through a camera placed in front of the wheelchair users. A different and newer ideal is the Tongue Drive System (TDS) developed by a team at Georgia Tech [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control Wheelchair Using a New Visual Joystick

Rabhi

Mrabet

Fnaiech

2018

Journal of Healthcare Engineering

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A new control system of a hand gesture-controlled wheelchair (EWC) is proposed. This smart control device is suitable for a large number of patients who cannot manipulate a standard joystick wheelchair. The movement control system uses a camera fixed on the wheelchair. The patient's hand movements are recognized using a visual recognition algorithm and artificial intelligence software; the derived corresponding signals are thus used to control the EWC in real time. One of the main features of this control technique is that it allows the patient to drive the wheelchair with a variable speed similar to that of a standard joystick. The designed device “hand gesture-controlled wheelchair” is performed at low cost and has been tested on real patients and exhibits good results. Before testing the proposed control device, we have created a three-dimensional environment simulator to test its performances with extreme security. These tests were performed on real patients with diverse hand pathologies in Mohamed Kassab National Institute of Orthopedics, Physical and Functional Rehabilitation Hospital of Tunis, and the validity of this intelligent control system had been proved.

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A brain and gaze-controlled wheelchair

Abstract: International audienceno abstrac

Cited by 15 publications

References 2 publications

Emotion detection for wheelchair navigation enhancement

Emotion detection for wheelchair navigation enhancement

Cerebral and gaze data fusion for wheelchair navigation enhancement: case of distracted users

Intelligent Control Wheelchair Using a New Visual Joystick

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