Interface Framework to Drive an Intelligent Wheelchair Using Facial Expressions

Faria, PedroMiguel; Braga, Rodrigo A. M.; Valgôde, Eduardo; Reis, Luís Paulo

doi:10.1109/isie.2007.4374877

Cited by 24 publications

(14 citation statements)

References 9 publications

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“…The facial expression input makes use of image-processing algorithms to detect features, such as color se gmentation and edge detection, followed by th e application of a neu ral network to detec t the user 's desire. The res ults shown in Faria et al [39] provide evidence that comfortably driving an IW with the use of facial expressions is possible. However, such input still de monstrates some limitations regarding color segmentation (much sensitivity to lar ge light variations and slight color shifts) and shape extraction (improve precision without increasing the processing time).…”

Section: Discussionmentioning

confidence: 96%

IntellWheels: Modular development platform for intelligent wheelchairs

Braga¹,

Petry²,

Reis³

et al. 2011

JRRD

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Abstract-Intelligent wheelchairs (IWs) can become an important solution to the challenge of assisting individuals who have disabilities and are thus unable to perform their daily activities using classic powered wheelchairs . This article describes the concept and design of IntellWheels, a modular platform to facilitate the development of IWs through a multiagent system paradigm. In fact, modularity is achieved not only in the software perspective, but also through a generic hardware framework that was designed to fit, in a straightforward manner, almost any commercial powere d wheelchair. Experimental results demonstrate the successful integration of all modules in the platform, providing safe motion to the IW. Furthermore, the results achieved with a prototype running in autonomous mode in simulated and mixed-reality environments also demonstrate the potential of our approach. Although some future research is still necessary to fully accomplish our objectives, preliminary tests have show n that IntellWheels will effectively reduce users' limitations, offering them a much more independent life.

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Section: Discussionmentioning

confidence: 96%

IntellWheels: Modular development platform for intelligent wheelchairs

Braga¹,

Petry²,

Reis³

et al. 2011

JRRD

Self Cite

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“…Typically, an IW is controlled by a computer, it has a set of sensors and applies techniques derived from mobile robotics research in order to process the sensor information and generate the motors commands. The interface may consist of a conventional wheelchair joystick, voice based control, facial expressions (Faria et al, 2007) or even vision control, among others. The concept of IW is different from a conventional electric wheelchair, since in this latter case the user takes manual control over motor speed and direction via a joystick or other switch, without intervention by the wheelchair's control system.…”

Section: Definition and Characteristics Of An Intelligent Wheelchairmentioning

confidence: 99%

Evaluation of Distinct Input Methods of an Intelligent Wheelchair in Simulated and Real Environments: A Performance and Usability Study

Faria

Vasconcelos

Reis

et al. 2013

Assistive Technology

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This paper focuses on evaluating the usability of an Intelligent Wheelchair (IW) in both real and simulated environments. The wheelchair is controlled at a highlevel by a flexible multimodal interface, using voice commands, facial expressions, head movements and joystick as its main inputs. A Quasiexperimental design was applied including a deterministic sample with a questionnaire that enabled to apply the System Usability Scale. The subjects were divided in two independent samples: 46 individuals performing the experiment with an Intelligent Wheelchair in a simulated environment (28 using different commands in a sequential way and 18 with the liberty to choose the command); 12 individuals performing the experiment with a real IW. The main conclusion achieved by this study is that the usability of the Intelligent Wheelchair in a real environment is higher than in the simulated environment. However there were not statistical evidences to affirm that there are differences between the real and simulated wheelchairs in terms of safety and control. Also, most of users considered the multimodal way of driving the wheelchair very practical and satisfactory. Thus, it may be concluded that the multimodal interfaces enables very easy and safe control of the IW both in simulated and real environments.

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“…2(b). If the number of pixels falling into a particular region with a certain (Cb, Cr) value exceeds a threshold value, that pixel is considered as a skin color and the image is transformed to a binary image F using (2).…”

Section: A Skin Color Segmentationmentioning

confidence: 99%

“…In [1], [2], numerous systems are proposed to control a robot or a wheelchair using head or face movement. Such systems involve body movement and are not suitable for people with extreme physical disabilities where head or face movement is difficult.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robot Control Using Facial Expressions

Mittal¹,

Srivastava²,

Mukherjee³

2012

IJCTE

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Abstract-In this paper, a novel real time robot control system using human facial expressions is presented. The proposed system mainly consists of three modules: face detection, facial expression recognition and robot control. The first module aims to find the user's face from the image captured from a live video through a series of steps including skin color classification, edge detection and mathematical morphology, while the second module analyses the detected face to recognize the facial expressions like happiness, sadness, surprise, anger and neutral using Principal Component Analysis (PCA) and Euclidian distance calculation. Finally, the detected facial expressions are used as controlling commands for the robot. Experiments were conducted for respective modules. 150 images under different lightening conditions and complex background were employed. In the experiment on detecting the facial region, the rate of detection was 99.3% and on recognizing the facial expression, the recognition rate was 97.3%. The robot was then controlled autonomously with an accuracy of 100%. Combining the above three rates, the overall rate of success of our system was 97.3%.

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Interface Framework to Drive an Intelligent Wheelchair Using Facial Expressions

Cited by 24 publications

References 9 publications

IntellWheels: Modular development platform for intelligent wheelchairs

IntellWheels: Modular development platform for intelligent wheelchairs

Evaluation of Distinct Input Methods of an Intelligent Wheelchair in Simulated and Real Environments: A Performance and Usability Study

Autonomous Robot Control Using Facial Expressions

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