claid 0.6.4

CLAID Python package (PyCLAID)

CLAID - Closing the Loop on AI & Data Collection: Flutter Package

A pip package containing the Python implementation of CLAID (PyCLAID). CLAID is a flexible and modular middleware framework based on transparent computing. CLAID allows to build applications for mobile (Android, WearOS, iOS*) and regular (Linux, macOS) operating systems, enabling seamless communication between individual Modules implemented in different programming languages (C++, Java, Dart, Python) running on all these operating systems. Existing CLAID Modules allow to effortlessly implement modular machine learning and data collection application with little-to-no coding. For more details, check out the CLAID website and our publication.

CLAID is developed and maintained by the Centre for Digital Health Interventions at ETH Zurich.

       

*iOS support available but not yet released

Features

• Seamless communication between Modules running on different OS or implemented in programming languages, allowing different devices to be integrated into an edge-cloud system
  • Support for Android, WearOS, Linux and macOS (iOS support in the making)
  • Support for C++, Java, Dart and Python*
• Pre-created Modules ready to use without programming, which can be loaded, configured and combined from simple configuration files:
  • Modules for data collection on Android and WearOS
  • Modules for data serialization, storage and upload
  • Modules to execute machine learning models (currently using TensorFlowLite)
• Background operating via services on Android and WearOS
• Encryption in rest and in-transit (soon) of data sent via a network or stored locally

*pip package will be released separately

Our research

CLAID is driven by our Digital Biomarker Research. In the field of Digital Biomarkers, we use mobile devices like Smartphones, Wearables, and Bluetooth Peripherals to gather datasets for training Machine Learning-based Digital Biomarkers. We observed a lack of tools to repurpose our data collection applications for real-world validation of our research projects. CLAID offers a unified solution for both data collection and integration of trained models, closing a critical gap in Digital Biomarker research.

If you are interested in our research and how we use CLAID to build mobile AI and Digital Biomarker applications, check out the ADAMMA group (Core for AI & Digital Biomarker, Accoustic and Inflammatory Biomarkers) at the Centre for Digital Health Interventions at ETH Zurich.

Source code availability

CLAID is completely open-sourced and released under the Apache2 license. You can access the code from the CLAID repo.

Issues, Feedback and Contribution

... coming soon

Contributors

Patrick Langer, ETH Zurich, 2023
Stephan Altmüller, ETH Zurich, 2023
Francesco Feher, ETH Zurich, University of Parma, 2023
Filipe Barata, ETH Zurich, 2023