ADAMMA - Core for AI & Digital Biomarker Research

Closing the Loop on AI- & Data Collection

CLAID is an open-source framework based on transparent computing which allows to building machine learning, data analysis and data collection applications across different devices, operating systems and programming languages. Using CLAID, you can samlessly deploy machine learning models and algorithms to edge- and cloud-devices. If you have existing code in Python, you can inject it into one of our Android, WearOS, or iOS applications, effectively deploying your model without having to write an App yourself. Besides, CLAID offers ready-to-use Modules for data collection, machine learning and visualizations, which you can combine using our CLAID Designer to create complex applications. CLAID is compatible with Android, iOS & WearOS as well as Linux & macOS and provides API bindings for Python, Java, Dart and C++, offering seamless (“transparent”) communication across different devices and programming languages.

CLAID is driven by our Digital Biomarker Research, using mobile devices like Smartphones, Wearables, and IoT to gather datasets for training and evaluation of Machine Learning-based Digital Biomarkers. We observed a lack of tools to easily deploy our Digital Biomarkers for validation in real-world scenarios. To that end, CLAID offers a unified solution for both data collection and deployment of trained models, a critical step in Digital Biomarker research. Beyond this domain, CLAID holds potential for Mobile Computing, Mobile AI, Edge Computing, Distributed Computing Data Collection applications scenarios.

Publications

CLAID: Closing the Loop on AI amp; Data Collection A cross-platform transparent computing middleware framework for smart edge-cloud and digital biomarker applications
CLAID: Closing the Loop on AI & Data Collection — A cross-platform transparent computing middleware framework for smart edge-cloud and digital biomarker applications
Patrick Langer, Stephan Altmüller, Elgar Fleisch, Filipe Barata
Future Generation Computer Systems  ·  01 Oct 2024  ·  doi:10.1016/j.future.2024.05.026
Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring From Activity, Heart Rate, and Core Body Temperature
Comparative Efficacy of Commercial Wearables for Circadian Rhythm Home Monitoring From Activity, Heart Rate, and Core Body Temperature
Fan Wu, Patrick Langer, Jinjoo Shim, Elgar Fleisch, Filipe Barata
IEEE Journal of Biomedical and Health Informatics  ·  01 Feb 2025  ·  doi:10.1109/jbhi.2024.3471254