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!
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 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!
@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 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 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 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
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! ✨
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
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.
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
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.