Spatial Blockchain-based Secure Mass Screening Framework for Children with Dyslexia

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Aim:

The main aim of this project is to find dyslexia symptoms and generate metric data for an individual user, community or group in general.

Existing System:

The Existing system has proposed various tests for identifying dyslexia in school children aged under 11 years. Dyslexia detection is a challenging prospect at the age of 4 or 5 when a child starts going to school. Since these gifted children have difficulty in reading, writing, and drawing, they fail to properly follow class lectures, prepare homework and perform in exams. Hence, dyslexic children begin to become isolated from other children, attain poor grades, and may even forego future studies and associated professional careers. Recent face, pupil, hand gesture, and voice recognition IoT devices can process data locally and upload the diagnosis data to the nearby MEC node. MEC shows the potential to address the availability and improved connectivity, resilience, scalability, low latency, and real-time delivery of massive amounts of data, which the traditional cloud-only solution fails to guarantee.

Proposed System:

In our proposed system the patient’s live eye and hand actions during the test is securely captured from the smart-phone or tab as a video, interweaved with the audio and spatio-temporal gaze focus. After the test finishes, the test module consisting of user drawings, the user-typed characters, and user interaction with the screen is saved as video and sent to the nearby MEC node. The MEC node employs auto-grading algorithms to find dyslexic patterns by analyzing multimedia IoT data, saving the final result in Blockchain and off-chain. The writing test is recorded as a video interaction session, which can be replayed at any time in the future, at any location, to retrace the writing technique specific for dyslexic patients.