NeuroHealth.Care provides an open‑source Web3 platform that lets parents, educators, and researchers run AI‑driven neuropsychological assessments and receive personalized cognitive training for children. The system records contributions on a blockchain to reward developers and share anonymized data for research, while offering a web interface for home use.
Funding
Funding not disclosed
Founders
Product
Problem
Children’s cognitive development often lacks accessible, personalized neuropsychological assessment and training tools, especially for families without specialist support. This limits early detection of cognitive issues and hampers evidence-based interventions that could improve long‑term outcomes.
Solution
NeuroHealth.Care offers an open‑source Web3 platform that connects neuropsychology educators, researchers, and parents to create and share AI‑driven diagnostic assessments and individualized training programs for children. The platform uses machine‑learning models to generate personalized cognitive profiles and recommends targeted exercises. All interactions and data contributions are recorded on a blockchain, ensuring transparent, fair reward distribution for developers, educators, and researchers based on their contributions. Aggregated, anonymized data are made available to the research community to advance neuropsychological methods and educational content. The system is designed to be usable by parents at home while maintaining clinical rigor.
Target Audience
Primary users are parents seeking evidence‑based cognitive development tools for their children, as well as neuropsychology educators and researchers who need a collaborative platform for diagnostics, training content, and data sharing.
Features
- AI-powered neuropsychological diagnostics that produce individualized cognitive ability reports for each child
- Adaptive training modules that adjust difficulty in real time based on performance metrics
- Open‑source codebase allowing developers to extend functionality and contribute improvements
- Blockchain‑based incentive model that rewards contributors (code, lessons, assessments) with tokenized compensation
- Secure, anonymized data aggregation for researchers to conduct large‑scale studies and develop new methodologies
- Web interface for parents to administer diagnostics, track progress, and access curated educational materials