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Detecting Readers with Dyslexia Using Machine Learning with Eye Tracking Measures

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To develop a system for remedial reading instruction that uses visually controlled auditory prompting to help the user with recognition and pronunciation of words. Examined audiovisual (AV) speech perception deficit in dyslexic readers. The eye-tracking methods were used for the first time to provide evidence of an AV speech perception deficit in dyslexic readers. Develop the first statistical model to predict readers with and without dyslexia using eye tracking measures. Developing a prototype without unnecessary complexities or distractions and by retaining and enhancing those features that are required, this type of e-Learning system can meet the needs of its intended users.

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The main objective is to help disabled people with the help of Human Computer Interaction Reading Assistant is a visually activated, uses eye tracking to trigger synthetic speech feedback as children read text from the monitor. Eyetracking data is used for find out the coordinates where eyes fixing. Word selection algorithm to trigger highlighting and pronunciation of words. The relationship between auditory speech perception, phonological processing skills, and reading skills was tested and discussed. The eye movements during perceiving of visual speech cues of audiovisual speech in dyslexic individuals. Gaze data will be collected while participants carry out the experimental tasks.

Human-computer interaction studies that use eye tracking with people with dyslexia have normally focused in finding the most accessible text presentations. Statistical model to classify readers with and without dyslexia using a Support Vector Machine binary classifier and how eye tracking measures have been studied in relationship with dyslexia. The creation process of a prototype e-Learning system for people with CLDs, with the goal of alleviating over complexity by reducing the number of features presented to the user at any one time, and by enhancing crucial features, without negatively affecting overall functionality.

Disabled users need interfaces that suit their skills and assist them in overcoming physical and cognitive barriers. Study of using Human computer interaction in assistive technology To assist the reader, multimedia educational software is available that will allow text, displayed on a computer screen, to be Sequentially highlighted and spoken by the computer. an ideal computer-based remediation tool would allow the student to concentrate on the reading task assisted by automated computer response as necessary.

Visual perception in dyslexics has primarily been examined for reading tasks or in various types of reading and non-reading tasks in the studies of visual attention impairments. Dyslexic readers have longer fixation durations, more fixations, shorter saccades and more regressions than normally. Early detection of risk of dyslexia is very extensive and comes from different fields such as cognitive neuroscience, psychology or biology. The eye movements of readers with dyslexia are different from regular readers. People with dyslexia as well as beginner readers, make longer fixations, more fixations, shorter saccades and more regressions than readers without dyslexia. HCI is particularly important for people with cognitive and learning disabilities. Primary goals of e-Learning is to improve access for all learners, such systems frequently have many features and options which can be too complicated for people with CLDs. Many people with CLDs experience difficulties with memory and information organization. It is possible to help disabled people to enhance their communication capabilities and to facilitate their independent life by means of computers. Computers provide interesting possibilities to help people with disabilities to enhance their social integration. The Reading Assistant extension includes an ASSIST ENGINE that determines when to highlight and pronounce a word using the algorithm described below in the section on fixations and dwell time. Words are represented as word objects with methods for drawing, highlighting, indicating their rectangular extent, and pronouncing themselves. A text object is an array of lines where each line is an array of words.

Syllable identification: The syllable identification task included three blocks; audiovisual in quiet, audiovisual in white noise and visual only.

Gaze behavior: Eye-movement analyses were conducted in order to investigate gaze behavior during the different syllable identification tasks.

Eye tracking measures and machine learning techniques to predict readers with dyslexia automatically. SVM algorithm is used for the analysis. An SVM is a method for supervised learning that analyzes data and recognize patterns for classification.

A study involving adolescents with high functioning autism was conducted to determine which type of interface supports better performance on a game to teach social skills. The learning disability was analyzed using a survey. Existing computer interaction style was mainly based on a standard keyboard and mouse for input, and output based on screen for data, a printer for hard copy, and a “bell” for some warnings and signals “patchwork” adaptation were not reusable for other users and they became useless when new incompatible devices and applications appeared. HCI is done a speedy development. Reading Assistant; a tool to evaluate the effectiveness of visually activated prompting in the context of a remedial reading program. Potential beneficiaries include the estimated 10 million children with dyslexia in the 50, 000 school districts in the U. S. , as well as large numbers of adults with learning disabilities.

Compared performance of adults with dyslexia with a control group of normal adult readers in AV and visual only speech perception, measured with syllable identification tasks and second, to compared basic eye movements, fixation time and number of fixations, during audiovisual speech perception between these two groups of participants. Indicates that the age of the users shows clearer differences in their reading performance. The main problem is the size of the dataset, since having more input data would lead our model to generalize better in those cases. High degree of usability, and imply that, for user, the interface would facilitate deriving educational benefit from the e-Learning system.

Human computer interaction is analysed in all variance like legal, social and ethical issues, the universal approach and all. Implemented a system which uses a reader’s visual scanning pattern of the text to identify, and pronounce, words that the reader is having difficulty recognizing. To provide evidence of impaired audiovisual speech perception in dyslexia by using eye-tracking. A specific deficit in the perception of visual speech cues in adult individuals with dyslexia. The eye movements of readers with dyslexia are different from regular readers. People with dyslexia have longer reading times, make longer fixations, and make more fixations than readers without dyslexia. And these characteristics can be used to train a machine learning model. A prototype web-based eLearning interface was developed to provide accessibility for users with CLDs. Essential functionality, while eliminating features that could cause unnecessary stress and frustration, thereby negatively affecting system usability and learning. Assistive Technology should evolve towards more general methods and theories, as the ones proposed by Human Computer Interaction and the Universal Design. Interface design for disabled people is greatly facilitated if computer designers have in mind the existence of users with different characteristics Improve the performance using different parametric approach and use this system regularly in students cognitive activities to get the maximum accurate result.

Limitation is that the study is included only university students. Enlarge the dataset by carrying out more eye tracking experiments in different languages. And try with classifiers, such as perceptron learning, recursive neural networks, or conditional random fields.

Difficult to perform and analyze, large, comparative studies are essential in assessing the efficacy of not only interfaces designed for people with CLDs. Technology and social pressure, can avoid this kind of digital divide between people with special needs and standard people. Assistive Technology should evolve towards more general methods and theories.

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