Spatial Blockchain-Based Secure Mass Screening Framework for Children With Dyslexia

• Main Authors: Md. Abdur Rahman, Elham Hassanain, Md. Mamunur Rashid, Stuart J. Barnes, M. Shamim Hossain
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Blockchain; dyslexia; auto-grading; mass screening; mobile multimedia health
• Online Access: https://ieeexplore.ieee.org/document/8488459/

In this paper, we present a novel method, process, and system for calculating dyslexic symptoms, generating metric data for an individual user, community, or group in general. We present a mobile multimedia Internet of Things (IoT)-based environment that can capture multimodal smartphone or tab-based user interaction data during dyslexia testing and share it via a mobile edge network, which employs auto-grading algorithms to find dyslexia symptoms. In addition to algorithm-based auto-grading, the captured mobile multimedia payload is stored in a decentralized repository that can be shared with a medical practitioner for replay and further manual analysis purposes. Since the framework is language-independent and based on Blockchain and a decentralized big data repository, dyslexic patterns and a massive amount of captured multimedia IoT test data can be shared for further clinical research, statistical analysis, and quality assurance. Notwithstanding, our proposed Blockchain and off-chain-based decentralized and secure dyslexia data storage, management, and sharing framework will allow security, anonymity, and multimodal visualization of the captured test data for mobile users. This paper presents the detailed design, implementation, and test results, which demonstrate the strong potential for wider adoption of the dyslexia mobile health management globally.