Development of the eye input device using eye movement obtained by measuring the center position of the pupil

Oyabu, Yuki; Takano, Hironobu; Nakamura, Keisuke

doi:10.1109/icsmc.2012.6378242

Cited by 6 publications

(13 citation statements)

References 5 publications

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“…In this category, the approach to target selection varies and can be based on the detection/identification of voluntary eye blinking (long eye-blinks) [ 65 ], the simulation of analogous mouse clicks (right or left eye-blink) [ 66 , 67 , 68 , 70 , 71 ] and the sequential combination of blinks in a temporal space [ 69 ]. With the same percentage, 28% of the primary studies provided Human–Computer Interaction approaches to Eye-Tracking, i.e., identifying and classifying the effective pupil direction [ 72 , 73 , 74 , 75 , 76 , 77 , 78 ]. Despite its similarity to Eye-Gaze, this strategy seeks to estimate gaze direction in relation to the image pixels, which is more accurate and goes beyond horizontal and vertical lines.…”

Section: Resultsmentioning

confidence: 99%

“…For enhancing the pupil (or iris) in an eye image, the robust and versatile technique for detecting circles called the Circular Hough Transform [ 58 , 62 , 63 , 73 , 74 ] is highlighted. Other techniques covering the eye detection/localization task can also be mentioned, such as the Limbus tracking method [ 59 ], gradient-based algorithms [ 60 , 80 ], Machine Learning models based on Hierarchical Temporal Memory [ 61 , 64 ], Erosion with a cross-shaped structure element [ 68 ], segmentation based on thresholding [ 69 ], and the Clustering Method of Unbroken Pixel Lines [ 76 ].…”

Section: Resultsmentioning

confidence: 99%

“…Rozado et al [ 61 ] and Rozado et al [ 64 ] combined Machine Learning models based on Hierarchical Temporal Memory with ITU Gaze Tracker (open source library software) and the Needleman–Wunsch algorithm, respectively. In a study by Oyabu et al [ 76 ], the pupil position was defined using the Clustering Method of Unbroken Pixel Lines. Also performing mathematical operations, Aharonson et al [ 75 ] calculated the pupil position using two different algorithms: a parametrical interpolation-based algorithm (called a polynomial) and a model-based algorithm (called a projection).…”

Section: Resultsmentioning

confidence: 99%

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Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have

Fernandes

Júnior

Alves

et al. 2023

JCM

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Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human–Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human–Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human–Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have

Fernandes

Júnior

Alves

et al. 2023

JCM

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“…Xindian Long, Ozan K. Tonguz and Alex Kiderman presented a high speed head mounted binocular eye tracking system that uses symmetric mass center algorithm to detect the eye position [4]. Yuki Oyabu, Hironobu Takano, Kiyomi Nakamura proposed a novel eye input device using the eye movement derived from the relative position of pupil which is [5]. In most of the above proposed systems, segmentation of the iris or pupil is the first step to determine the gaze of the user.…”

Section: Introductionmentioning

confidence: 98%

Estimation of gaze for human computer interaction

Sambrekar¹,

Ramdasi²

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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Gaze of the human eye may act as a communication medium for limb disabled people to interact with computers. In order to determine gaze direction, the first necessary step in image processing is segmentation of iris from entire eye image. In this paper Daugman's algorithm is implemented that uses Integro Differential Operator (IDO) to differentiate iris boundary from sclera part of eye and to find the centroid, to determine where the person is looking. Also the computation time required for IDO to scan the image is minimized by applying the operator only to the local minima pixels in the image. The algorithm is implemented in MATLAB and tested on static eye images selected from the CASIA database as well as on the eye images captured by an iPhone 4s, 8 MP camera with a resolution of 3264×2448 pixels. This method can be useful in eye tracking to handle the input devices of computer like keyboard and mouse.

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“…Xindian Long et al presented a high speed head mounted binocular eye tracking system that uses a symmetric mass center algorithm to detect the eye position [4]. Yuki Oyabu et al proposed a novel eye input device using the eye movement derived from the relative position of the pupil which is estimated from the characteristics of unbroken pixel line [5]. There are some other systems like ERICA and Tobii but most of the existing techniques involve complex hardware, less accuracy and high cost.…”

Section: Introductionmentioning

confidence: 99%

Human computer interaction for disabled using eye motion tracking

Sambrekar¹,

Ramdasi²

2015

2015 International Conference on Information Processing (ICIP)

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Human Computer Interaction is a trend-in technology. Working in this field, we have developed a system to provide a solution to the limb disabled people to interact with computer. Different algorithms are implemented in this paper, to estimate the gaze of user to recognize the reference key. The first step performed on each input frame is the face detection, which is achieved using Viola-Jones algorithm, whereas Circular Hough Transform is used to locate the pupil in eye image. Similarly glint (a small and intense dot inside the pupil image) is detected by the blob analysis method. Using these pupil location and glint location, gaze direction of the user is estimated. With these functions, the user is able to handle input devices of a computer like keyboard and helps to recognize the key to be pressed. Further the type-in process is continued with the blinking phenomenon which is detected using template matching method. Currently the system is limited to 4-key keypad. The system is also designed with opening some window application softwares like Skype, media player etc. which are useful to the limb disable people. The camera used in this application, to access video is Logitech HD 720p webcam (C310) and the software implementation part is done in MATLAB. The system is tested under various environmental conditions, giving the appreciable results and speed.

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Development of the eye input device using eye movement obtained by measuring the center position of the pupil

Cited by 6 publications

References 5 publications

Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have

Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have

Estimation of gaze for human computer interaction

Human computer interaction for disabled using eye motion tracking

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