RADAR-base Documentation Hub
Summary
About RADAR-base
Installing RADAR-base
- Component overview and breakdown
- How to get started with tools around RADAR-Kubernetes
- Setting up a Kubernetes Cluster for RADAR-base
Management Portal
Mobile Apps (pRMT and aRMT)
- Passive Remote Monitoring App
- Integrating new Apps and Devices into RADAR-base platform
- Other mHealth Platforms
- Integrated Devices Additional Configuration
- Active Questionnaire App
Backend
- RADAR Backend Monitors
- RADAR Backend Streams
- RESTful API Data Collection (WiP)
- RADAR-base REST-API
- Storage
- Kafka
Schemas
Catalogue of Devices and Data Sources
REDCap integration
- REDcap-integration Docker deploy
- REDcap Server and RADAR-base Integration confg
- REDCap-integration configuration
- REDcap-integration Additional Information
Security
- Security Vulnerability from Management Portal REST API
- Log4Shell (CVE-2021-44228) Zero-day vulnerability effect on RADAR-base report
Java Docs
Dashboards
Data Extraction
Support Desk Tickets
Data Issues and Troubleshooting
- aRMT Early Pilot Data for RADAR-CNS
- pRMT Troubleshooting
- Data Info and Missing Data
- Duplicate Data Points
- List of Incompatible Phones and Potential Fixes
- Additional phone setting setup for Android 9 (P or later)
- Garmin Troubleshooting
- RADAR-CNS Significant Events Tracker
How-to articles
Upgrades on https://radar-cns-platform.rosalind.kcl.ac.uk/
Good First Issue for New Developers
Quick Start Deployment
RADAR-base Improvement Proposals
Untitled live doc 2025-08-11
Untitled whiteboard 2025-08-11
About
RADAR-base is an open-source platform for remote, continuous, and scalable health data collection. It integrates data from smartphones, wearables, apps, and IoT devices, allowing researchers to securely collect, manage, and analyze real-time data.
Sponsored by: IMI2 Horizon 2020 mHealth Data Collection Platform (smartphone + wearable sensors)
🔗 Learn more at: https://www.radar-base.org/.
Mission
Our mission is to improve people’s quality of life by leveraging clinical value from wearable sensor and smartphone data.
Researchers studying diseases with machine learning (ML) and artificial intelligence (AI) require increasingly large datasets.
Smartphones now provide a unique opportunity for remote, continuous, real-time data collection at scale.
We developed RADAR-base to:
Enable scalable digital health data collection
Empower researchers with modern tools for AI-ready datasets
Create opportunities for the public to contribute as citizen scientists in health research
Core team
Amos Folarin – KCL, UCL, SLAM (Project Lead)
Julia Kurps – The Hyve (Project Lead)
Richard Dobson – KCL (Project Lead)
Yatharth Ranjan – KCL (Software Engineer)
Pauline Conde – KCL (Software Developer)
Joris Borgdorff – The Hyve
Zulqarnian Rashid – KCL
Nivethika Mahasivam – The Hyve
Maxim Moinat – The Hyve
Maximilian Kerz – KCL
Denny Verbeeck – Janssen R&D
Heet Sankesara – KCL
Akash Roy Ch – KCL