Backwards Maneuvering Powered Wheelchairs with Haptic Guidance

Poorten, Emmanuel Vander; Demeester, Eric; Hüntemann, Alexander; Reekmans, Eli; Philips, Johan; Schutter, Joris De

doi:10.1007/978-3-642-31401-8_38

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…1 Many studies incorporated this paradigm in order to conceive a suitable wheelchair according to the subject pathology. Vander Poorten et al 2 established a bilateral communication channel between the wheelchair controller and the user based on haptic feedback; the local map of the wheelchair environment was built through the on-board sensors. This leads to the introduction of haptic collision avoidance and haptic obstacle avoidance algorithms that help the user to successfully maneuver the wheelchair backwards inside an elevator.…”

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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“…New wheelchairs integrate options and adaptive technologies to fit the user’s morphology and pathology [ 3 , 4 ]. While some projects focused on adapting wheelchair navigation in outdoor environments (such as [ 5 ]), others targeted mainly indoor one’s [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Muscular and Cerebral Physiological Indices Assessment for Stress Measuring during Virtual Wheelchair Guidance

Khelifa

Lamti²,

Hugel³

2021

Brain Sciences

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The work presented in this manuscript has the purpose to assess the relationship between human factors and physiological indices. We discuss the relationship between stress as human factor and cerebral and muscular signals as features. Ten male paraplegic, right-handed subjects were volunteers for the experiment (mean age 34 ±6). They drove a virtual wheelchair in an indoor environment. They filled five missions where, in each one, an environmental parameter was changed. Meanwhile, they were equipped with Electromyography (EMG) sensors and Electroencephalography (EEG). Frequency and temporal features were filtered and extracted. Principal component analysis (PCA), Fisher’s tests, repeated measure Anova and post hoc Tukey test (α = 0.05) were implemented for statistics. Environmental modifications are subject to induce stress, which impacts muscular and cerebral activities. While the time pressure parameter was the most influent, the transition from static to moving obstacles (avatars), tends to have a significant impact on stress levels. However, adding more moving obstacles did not show any impact. A synchronization factor was noticed between cerebral and muscular features in higher stress levels. Further examination is needed to assess EEG reliability in these situations.

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“…New wheelchairs integrate adaptive technologies and options to fit the pathology, morphology, environment and to daily and professional activities of the user. 3 Some projects [4][5][6][7] propose new techniques (self-localization, obstacle avoidance, motion scheming, etc.) to enhance wheelchair navigation experience.…”

Section: Introductionmentioning

confidence: 99%

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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Backwards Maneuvering Powered Wheelchairs with Haptic Guidance

Cited by 5 publications

References 7 publications

Emotion detection for wheelchair navigation enhancement

Emotion detection for wheelchair navigation enhancement

A Muscular and Cerebral Physiological Indices Assessment for Stress Measuring during Virtual Wheelchair Guidance

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

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