A practical efficient human computer interface based on saccadic eye movements for people with disabilities

Soltani, Sima; Mahnam, Amin

doi:10.1016/j.compbiomed.2016.01.012

Cited by 31 publications

(11 citation statements)

References 35 publications

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“…Using the nearest neighbor algorithm, they classified eye movement in four directions and two types of blinking with 95% accuracy. In addition, there are several studies that developed goggle-type wearable EOG measurement systems to improve the user’s comfort by reducing preparation time [ 11 , 12 , 13 ]. However, the electrodes around the eyes often cause discomfort to the user, and this discomfort has been the motivation for many studies [ 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

Heo

Yoon

Park

2017

Sensors

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Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain–computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles.

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Section: Introductionmentioning

confidence: 99%

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

Heo

Yoon

Park

2017

Sensors

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“…Saccade represents a change of attention from one point to other point with 30-80 ms of duration. Saccadic eye movement has been used as a gaze-based control for video games that require quick navigation [11], [12]. *) Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Similarity measures of object selection in interactive applications based on smooth pursuit eye movements

Herlina

Wibirama

Ardiyanto

2018

2018 International Conference on Information and Communications Technology (ICOIACT)

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Recognition of Hijaiyah Letter for Children Based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) Method 3D.5 404 The Development of Quail Eggs Smart Incubator for Hatching System based on Microcontroller and Internet of Things (IoT) 6D.6 920 Low-Cost Portable Spectrometer for Lard Detection based on SVM Method Anindra, Firman 3A.4 267 Improving the Quality of Enterprise IT Goals using COBIT 5 Prioritisation Approach 6C.2 858 CCTV Traffic congestion analysis at Pejompongan using case based reasoning

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“…Typically, information regarding whether an eye is open or closed is used for gaze tracking systems [ 1 ], and various multimodal computer interfaces. For example, in order to measure brainwaves or accurately track eye movement, we can exclude brainwave signals or eye movements when the eyes are closed [ 2 , 3 ]. Furthermore, based on the results of studies that state that the blinking of Parkinson’s disease patients is slower than that of healthy people, and that of Tourette syndrome patients is faster, it is possible to predict such diseases based on the speed of opening and closing of the eyes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

Kim

Hong

Nam

et al. 2017

Sensors

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The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods.

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A practical efficient human computer interface based on saccadic eye movements for people with disabilities

Cited by 31 publications

References 35 publications

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

Similarity measures of object selection in interactive applications based on smooth pursuit eye movements

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

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