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Welcome to RAPIDS documentation

Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract and create behavioral features (a.k.a. digital biomarkers), visualize mobile sensor data, and structure your analysis into reproducible workflows.

RAPIDS is open source, documented, modular, tested, and reproducible. At the moment, we support data streams logged by smartphones, Fitbit wearables, and, in collaboration with the DBDP, Empatica wearables (but you can add your own too).

If you want to know more head over to Overview

Tip

Questions or feedback can be posted on the #rapids channel in AWARE Framework's slack.

Bugs and feature requests should be posted on Github.

Join our discussions on our algorithms and assumptions for feature processing.

Are you upgrading from RAPIDS 0.4.x or older? Follow this guide

Ready? Go to Overview.

What are the benefits of using RAPIDS?

  1. Consistent analysis. Every participant sensor dataset is analyzed in the same way and isolated from each other.
  2. Efficient analysis. Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated.
  3. Parallel execution. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code.
  4. Code-free features. Extract any of the behavioral features offered by RAPIDS without writing any code.
  5. Extensible code. You can easily add your own data streams or behavioral features in R or Python, share them with the community, and keep authorship and citations.
  6. Timezone aware. Your data is adjusted to one or more time zones per participant.
  7. Flexible time segments. You can extract behavioral features on time windows of any length (e.g., 5 minutes, 3 hours, 2 days), on every day or particular days (e.g., weekends, Mondays, the 1st of each month, etc.), or around events of interest (e.g., surveys or clinical relapses).
  8. Tested code. We are continually adding tests to make sure our behavioral features are correct.
  9. Reproducible code. If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended, thanks to R and Python virtual environments. You can share your analysis code along with your publications without any overhead.
  10. Private. All your data is processed locally.