RAPIDS

Reproducible Analysis Pipeline for Data Streams

Smartphone and wearable digital biomarkers

By MoSHI @PittTweet

[RAPIDS @RAPIDS_Science]

RAPIDS v1.9.0 is out with bug fixes, optimized memory usage and new steps volatility features!

[NuRelm @NuRelm]

Thank YOU @RAPIDS_Science for a great tool that turns #PassiveSensorData into a form that helps people make sense of their moods and their world!

[RAPIDS @RAPIDS_Science]

RAPIDS v1.8.0 is out with bug fixes and a new community contribution by @NuRelm adding support to process smartphone data collected with @awareframework micro!

[carissa low @carissa_low]

New paper led by @julio_ui describes @RAPIDS_Science, which makes the data processing required to translate raw continuous sensor data streams into interpretable behavioral features more efficient, reproducible, & transparent: https://t.co/jLztNmse6I

[RAPIDS @RAPIDS_Science]

RAPIDS 1.6.0 is out:

- Phone calls and location features can now be computed based on episodes
- Various bug fixes

[RAPIDS @RAPIDS_Science]

RAPIDS 1.5.0 is out:
- Updated Barnett location features with a faster Python implementation
- Other bug fixes and tests

Many thanks to @shirley_ah_ and Ian Barnett from @Penn for this new community contribution!

[RAPIDS @RAPIDS_Science]

@SBMDigitalHlth 🤚RAPIDS is open-source and documented software that speeds up the process to extract behavioral features (potential digital biomarkers) from smartphone and wearable data. We currently support @awareframework @fitbit and @empatica devices

[RAPIDS @RAPIDS_Science]

RAPIDS 1.4.0 is out:

- New features for application usage episodes
- We updated our visualizations
- Added tests and minor bug fixes for Fitbit, calls, WiFi, Bluetooth, conversation, and Activity Recognition data.
- Updated VSCode setup instructions for our Docker image

[RAPIDS @RAPIDS_Science]

RAPIDS 1.3.0 is out:
- Refactor locations DORYAB provider (up to 30x faster)
- New phone keyboard features
- New strategy to infer home location that can handle multiple homes for the same participant
- Exclude sleep episodes from steps intraday features

And other bug fixes!

[RAPIDS @RAPIDS_Science]

RAPIDS 1.2.0 is out:

- Sleep summary and intraday features are more consistent.
- Add wake and bedtime features for sleep summary data.
- Fix bugs with sleep PRICE features.
- Update home page with RAPIDS users and contributors
- Add contributing guide

[RAPIDS @RAPIDS_Science]

@isaacmoshe et al. used RAPIDS's smartphone features to create models to predict symptoms of depression and anxiety

[Isaac Moshe @isaacmoshe]

Our new @FrontPsychiatry paper is out! “Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data”

https://t.co/majv7hMKkW

Thx to @yannikterhorst, @gyidiasare @BosseSander @denzilferreira @HaraldBaumeiste @DavidCMohr @LRaback for a great collaboration! ✨

[RAPIDS @RAPIDS_Science]

Features (digital biomarkers) are based on activity level episodes, moderate-to-vigorous physical activity (MVPA) episodes, and low and high MET episodes. https://t.co/J0DpP6ewPs

[RAPIDS @RAPIDS_Science]

RAPIDS 1.1.0 is out with new features for @fitbit 's calories intraday data. These complement those available for steps, heart rate, and sleep data.

[RAPIDS @RAPIDS_Science]

RAPIDS 1.0.1 is out with fixes for multi-time zone studies and other minor bugs

[RAPIDS @RAPIDS_Science]

7/7 Other improvements include adopting a Code of Conduct, this Twitter account, user comments on the docs, a simplified minimal example, and stability and speed fixes.

[RAPIDS @RAPIDS_Science]

1/ We are releasing RAPIDS v1.0, a significant milestone with multiple useful features and community contributions that broaden, simplify and speed up mobile sensor data processing and analysis. 🧵https://t.co/yDI0htKs73

[RAPIDS @RAPIDS_Science]

6/ RAPIDS can now analyze data collected over multiple time zones for the same or different participants. Also, we have new Fitbit sleep intraday features contributed by CMU's Stephen Price.