Cited by 68Blockchain Technology Applications and SecurityBlockchain Technology in Education and LearningIoT and Edge/Fog Computing

Spatial blockchain-based secure mass screening framework for children with dyslexia

Open access
Rahman, Md. Abdur, Hassanain, Elham, Rashid, Md. Mamunur, Barnes, Stuart J., Hossain, M. Shamim·2018·IEEE Access

Abstract

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.

Integrity signals

Registry

Crossref · 10.1109/access.2018.2875242

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

1 of the 48 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Rahman, M. A., Hassanain, E., Rashid, M. M., Barnes, S. J., & Hossain, M. S.
Affiliations
  • King's College London (GB)
  • King Saud University (SA)
Venue
IEEE Access
Published
2018
Volume
6
Pages
61876-61885
ISSN
2169-3536 (ISSN-L)
OpenAlex
W2896338146
Cited by
68
References
48
Citations by year
2026: 3 · 2025: 3 · 2024: 5 · 2023: 10 · 2022: 8 · 2021: 16 · 2020: 18 · 2019: 5
FWCI
15.73
Type
journal article
Language
en
Open access
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Research Portal (King's College London) (submittedVersion, other-oa)
Funders
  • CODA Research Centre, King’s Business School, King’s College London, UK
  • Deanship of Scientific Research, King Saud University
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
dyslexia screening, blockchain, decentralized storage, mobile health, Internet of Things, children, health data sharing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review