77 lines
6.0 KiB
Markdown
77 lines
6.0 KiB
Markdown
# Welcome to RAPIDS documentation
|
|
|
|
Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to [extract](features/feature-introduction.md) and [create](features/add-new-features.md) **behavioral features** (a.k.a. digital biomarkers), [visualize](visualizations/data-quality-visualizations.md) mobile sensor data, and [structure](analysis/complete-workflow-example.md) your analysis into reproducible workflows.
|
|
|
|
RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support [data streams](datastreams/data-streams-introduction) logged by smartphones, Fitbit wearables, and Empatica wearables (the latter in collaboration with the [DBDP](https://dbdp.org/)).
|
|
|
|
!!! tip "Where do I start?"
|
|
|
|
:material-power-standby: New to RAPIDS? Check our [Overview + FAQ](setup/overview/) and [minimal example](analysis/minimal)
|
|
|
|
:material-play-speed: [Install](setup/installation), [configure](setup/configuration), and [execute](setup/execution) RAPIDS to [extract](features/feature-introduction.md) and [plot](visualizations/data-quality-visualizations.md) behavioral features
|
|
|
|
:material-github: Bugs should be reported on [Github issues](https://github.com/carissalow/rapids/issues)
|
|
|
|
:fontawesome-solid-tasks: Questions, discussions, feature requests, and feedback can be posted on our [Github discussions](https://github.com/carissalow/rapids/discussions)
|
|
|
|
:material-twitter: Keep up to date with our [Twitter feed](https://twitter.com/RAPIDS_Science) or [Slack channel](http://awareframework.com:3000/)
|
|
|
|
:material-plus-network: Do you want to modify or add new functionality to RAPIDS? Check our [contributing guide](./contributing)
|
|
|
|
:fontawesome-solid-sync-alt: Are you upgrading from RAPIDS `0.4.x` or older? Follow this [guide](migrating-from-old-versions)
|
|
|
|
|
|
## 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.
|
|
5. **Parallel execution**. Thanks to [Snakemake](https://snakemake.github.io/), your analysis can be executed over multiple cores without changing your code.
|
|
6. **Code-free features**. Extract any of the behavioral features offered by RAPIDS without writing any code.
|
|
7. **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.
|
|
8. **Time zone aware**. Your data is adjusted to one or more time zones per participant.
|
|
9. **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).
|
|
10. **Tested code**. We are continually adding tests to make sure our behavioral features are correct.
|
|
11. **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.
|
|
12. **Private**. All your data is processed locally.
|
|
|
|
## Users and Contributors
|
|
|
|
??? quote "Community Contributors"
|
|
Many thanks to the [whole team](./team) and our community contributions:
|
|
|
|
- Agam Kumar (CMU)
|
|
- Yasaman S. Sefidgar (University of Washington)
|
|
- Joe Kim (Duke University)
|
|
- Brinnae Bent (Duke University)
|
|
- Stephen Price (CMU)
|
|
- Neil Singh (University of Virginia)
|
|
|
|
Many thanks to the researchers that made [their work](./citation) open source:
|
|
|
|
- Panda et al. [paper](https://pubmed.ncbi.nlm.nih.gov/31657854/)
|
|
- Stachl et al. [paper](https://www.pnas.org/content/117/30/17680)
|
|
- Doryab et al. [paper](https://arxiv.org/abs/1812.10394)
|
|
- Barnett et al. [paper](https://doi.org/10.1093/biostatistics/kxy059)
|
|
- Canzian et al. [paper](https://doi.org/10.1145/2750858.2805845)
|
|
|
|
|
|
??? quote "Publications using RAPIDS"
|
|
- Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data [link](https://www.frontiersin.org/articles/10.3389/fpsyt.2021.625247/full)
|
|
- Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study [link](https://mhealth.jmir.org/2021/7/e26540)
|
|
- Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study [link](https://cancer.jmir.org/2021/2/e27975/)
|
|
- An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data [link](https://ieeexplore.ieee.org/abstract/document/9483755/)
|
|
- Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity [link](https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract)
|
|
|
|
<div class="users">
|
|
<div><img alt="carnegie mellon university" loading="lazy" src="./img/logos/cmu.png" /></div>
|
|
<div><img alt="digital biomarker development pipeline" loading="lazy" src="./img/logos/dbdp.png" /></div>
|
|
<div><img alt="university of helsinki" loading="lazy" src="./img/logos/helsinki.jpg" /></div>
|
|
<div><img alt="university of manchester" loading="lazy" src="./img/logos/manchester.png" /></div>
|
|
<div><img alt="monash university" loading="lazy" src="./img/logos/monash.jpg" /></div>
|
|
<div><img alt="oulu university" loading="lazy" src="./img/logos/oulu.png" /></div>
|
|
<div><img alt="university of pennsylvania" loading="lazy" src="./img/logos/penn.png" /></div>
|
|
<div><img alt="university of pittsburgh" loading="lazy" src="./img/logos/pitt.png" /></div>
|
|
<div><img alt="university of virginia" loading="lazy" src="./img/logos/virginia.jpg" /></div>
|
|
<div><img alt="university of washington" loading="lazy" src="./img/logos/uw.jpg" /></div>
|
|
</div>
|