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aware_influxdb (beta)
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Fitbit
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Empatica
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Mandatory Empatica Format
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Behavioral Features
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Phone Accelerometer
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Phone Activity Recognition
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Phone Applications Crashes
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Phone Applications Foreground
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Phone Applications Notifications
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Phone Battery
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Phone Bluetooth
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Phone Calls
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Phone Conversation
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Phone Data Yield
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Phone Keyboard
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Phone Light
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Phone Locations
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Phone Log
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Phone Messages
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Empatica
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Visualizations
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Data Quality
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Features
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Analysis Workflows
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Test cases
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What are the benefits of using RAPIDS?
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< svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z" / > < / svg >
< / a >
< h1 id = "welcome-to-rapids-documentation" > Welcome to RAPIDS documentation< a class = "headerlink" href = "#welcome-to-rapids-documentation" title = "Permanent link" > ¶ < / a > < / h1 >
< p > Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to < a href = "features/feature-introduction/" > extract< / a > and < a href = "features/add-new-features/" > create< / a > < strong > behavioral features< / strong > (a.k.a. digital biomarkers), < a href = "visualizations/data-quality-visualizations/" > visualize< / a > mobile sensor data, and < a href = "workflow-examples/analysis/" > structure< / a > your analysis into reproducible workflows.< / p >
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< p > RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support < a href = "datastreams/data-streams-introduction" > data streams< / a > logged by smartphones, Fitbit wearables, and Empatica wearables (the latter in collaboration with the < a href = "https://dbdp.org/" > DBDP< / a > ). < / p >
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< div class = "admonition tip" >
< p class = "admonition-title" > Where do I start?< / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M13 3h-2v10h2V3m4.83 2.17-1.42 1.42A6.944 6.944 0 0 1 19 12a7 7 0 0 1-7 7A6.995 6.995 0 0 1 7.58 6.58L6.17 5.17a9.001 9.001 0 0 0-1.03 12.69c3.22 3.78 8.9 4.24 12.69 1.02A9.003 9.003 0 0 0 21 12c0-2.63-1.16-5.13-3.17-6.83z" / > < / svg > < / span > New to RAPIDS? Check our < a href = "setup/overview/" > Overview + FAQ< / a > and < a href = "workflow-examples/minimal" > minimal example< / a > < / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M13 2.05v2c4.39.54 7.5 4.53 6.96 8.92-.46 3.64-3.32 6.53-6.96 6.96v2c5.5-.55 9.5-5.43 8.95-10.93-.45-4.75-4.22-8.5-8.95-8.97v.02M5.67 19.74A9.994 9.994 0 0 0 11 22v-2a8.002 8.002 0 0 1-3.9-1.63l-1.43 1.37m1.43-14c1.12-.9 2.47-1.48 3.9-1.68v-2c-1.95.19-3.81.94-5.33 2.2L7.1 5.74M5.69 7.1 4.26 5.67A9.885 9.885 0 0 0 2.05 11h2c.19-1.42.75-2.77 1.64-3.9M4.06 13h-2c.2 1.96.97 3.81 2.21 5.33l1.42-1.43A8.002 8.002 0 0 1 4.06 13M10 16.5l6-4.5-6-4.5v9z" / > < / svg > < / span > < a href = "setup/installation" > Install< / a > , < a href = "setup/configuration" > configure< / a > , and < a href = "setup/execution" > execute< / a > RAPIDS to < a href = "features/feature-introduction/" > extract< / a > and < a href = "visualizations/data-quality-visualizations/" > plot< / a > behavioral features< / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M12 2A10 10 0 0 0 2 12c0 4.42 2.87 8.17 6.84 9.5.5.08.66-.23.66-.5v-1.69c-2.77.6-3.36-1.34-3.36-1.34-.46-1.16-1.11-1.47-1.11-1.47-.91-.62.07-.6.07-.6 1 .07 1.53 1.03 1.53 1.03.87 1.52 2.34 1.07 2.91.83.09-.65.35-1.09.63-1.34-2.22-.25-4.55-1.11-4.55-4.92 0-1.11.38-2 1.03-2.71-.1-.25-.45-1.29.1-2.64 0 0 .84-.27 2.75 1.02.79-.22 1.65-.33 2.5-.33.85 0 1.71.11 2.5.33 1.91-1.29 2.75-1.02 2.75-1.02.55 1.35.2 2.39.1 2.64.65.71 1.03 1.6 1.03 2.71 0 3.82-2.34 4.66-4.57 4.91.36.31.69.92.69 1.85V21c0 .27.16.59.67.5C19.14 20.16 22 16.42 22 12A10 10 0 0 0 12 2z" / > < / svg > < / span > Bugs should be reported on < a href = "https://github.com/carissalow/rapids/issues" > Github issues< / a > < / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 512 512" > < path d = "M139.61 35.5a12 12 0 0 0-17 0L58.93 98.81l-22.7-22.12a12 12 0 0 0-17 0L3.53 92.41a12 12 0 0 0 0 17l47.59 47.4a12.78 12.78 0 0 0 17.61 0l15.59-15.62L156.52 69a12.09 12.09 0 0 0 .09-17zm0 159.19a12 12 0 0 0-17 0l-63.68 63.72-22.7-22.1a12 12 0 0 0-17 0L3.53 252a12 12 0 0 0 0 17L51 316.5a12.77 12.77 0 0 0 17.6 0l15.7-15.69 72.2-72.22a12 12 0 0 0 .09-16.9zM64 368c-26.49 0-48.59 21.5-48.59 48S37.53 464 64 464a48 48 0 0 0 0-96zm432 16H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16zm0-320H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16V80a16 16 0 0 0-16-16zm0 160H208a16 16 0 0 0-16 16v32a16 16 0 0 0 16 16h288a16 16 0 0 0 16-16v-32a16 16 0 0 0-16-16z" / > < / svg > < / span > Questions, discussions, feature requests, and feedback can be posted on our < a href = "https://github.com/carissalow/rapids/discussions" > Github discussions< / a > < / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M22.46 6c-.77.35-1.6.58-2.46.69.88-.53 1.56-1.37 1.88-2.38-.83.5-1.75.85-2.72 1.05C18.37 4.5 17.26 4 16 4c-2.35 0-4.27 1.92-4.27 4.29 0 .34.04.67.11.98C8.28 9.09 5.11 7.38 3 4.79c-.37.63-.58 1.37-.58 2.15 0 1.49.75 2.81 1.91 3.56-.71 0-1.37-.2-1.95-.5v.03c0 2.08 1.48 3.82 3.44 4.21a4.22 4.22 0 0 1-1.93.07 4.28 4.28 0 0 0 4 2.98 8.521 8.521 0 0 1-5.33 1.84c-.34 0-.68-.02-1.02-.06C3.44 20.29 5.7 21 8.12 21 16 21 20.33 14.46 20.33 8.79c0-.19 0-.37-.01-.56.84-.6 1.56-1.36 2.14-2.23z" / > < / svg > < / span > Keep up to date with our < a href = "https://twitter.com/RAPIDS_Science" > Twitter feed< / a > or < a href = "http://awareframework.com:3000/" > Slack channel< / a > < / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M16 11V9h-3V6h-2v3H8v2h3v3h2v-3h3m1-8a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h-4v2h1a1 1 0 0 1 1 1h7v2h-7a1 1 0 0 1-1 1h-4a1 1 0 0 1-1-1H2v-2h7a1 1 0 0 1 1-1h1v-2H7a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h10z" / > < / svg > < / span > Do you want to modify or add new functionality to RAPIDS? Check our < a href = "./contributing" > contributing guide< / a > < / p >
< p > < span class = "twemoji" > < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 512 512" > < path d = "M370.72 133.28C339.458 104.008 298.888 87.962 255.848 88c-77.458.068-144.328 53.178-162.791 126.85-1.344 5.363-6.122 9.15-11.651 9.15H24.103c-7.498 0-13.194-6.807-11.807-14.176C33.933 94.924 134.813 8 256 8c66.448 0 126.791 26.136 171.315 68.685L463.03 40.97C478.149 25.851 504 36.559 504 57.941V192c0 13.255-10.745 24-24 24H345.941c-21.382 0-32.09-25.851-16.971-40.971l41.75-41.749zM32 296h134.059c21.382 0 32.09 25.851 16.971 40.971l-41.75 41.75c31.262 29.273 71.835 45.319 114.876 45.28 77.418-.07 144.315-53.144 162.787-126.849 1.344-5.363 6.122-9.15 11.651-9.15h57.304c7.498 0 13.194 6.807 11.807 14.176C478.067 417.076 377.187 504 256 504c-66.448 0-126.791-26.136-171.315-68.685L48.97 471.03C33.851 486.149 8 475.441 8 454.059V320c0-13.255 10.745-24 24-24z" / > < / svg > < / span > Are you upgrading from RAPIDS < code > 0.4.x< / code > or older? Follow this < a href = "migrating-from-old-versions" > guide< / a > < / p >
< / div >
< h2 id = "what-are-the-benefits-of-using-rapids" > What are the benefits of using RAPIDS?< a class = "headerlink" href = "#what-are-the-benefits-of-using-rapids" title = "Permanent link" > ¶ < / a > < / h2 >
< ol >
< li > < strong > Consistent analysis< / strong > . Every participant sensor dataset is analyzed in the same way and isolated from each other.< / li >
< li > < strong > Efficient analysis< / strong > . Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated.< / li >
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< li > < strong > Parallel execution< / strong > . Thanks to < a href = "https://snakemake.github.io/" > Snakemake< / a > , your analysis can be executed over multiple cores without changing your code.< / li >
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< li > < strong > Code-free features< / strong > . Extract any of the behavioral features offered by RAPIDS without writing any code.< / li >
< li > < strong > Extensible code< / strong > . 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.< / li >
< li > < strong > Time zone aware< / strong > . Your data is adjusted to one or more time zones per participant.< / li >
< li > < strong > Flexible time segments< / strong > . 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 1< sup > st< / sup > of each month, etc.), or around events of interest (e.g., surveys or clinical relapses).< / li >
< li > < strong > Tested code< / strong > . We are continually adding tests to make sure our behavioral features are correct.< / li >
< li > < strong > Reproducible code< / strong > . 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.< / li >
< li > < strong > Private< / strong > . All your data is processed locally.< / li >
< / ol >
< h2 id = "users-and-contributors" > Users and Contributors< a class = "headerlink" href = "#users-and-contributors" title = "Permanent link" > ¶ < / a > < / h2 >
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< details class = "quote" > < summary > Community Contributors< / summary > < p > Many thanks to the < a href = "./team" > whole team< / a > and our community contributions:< / p >
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< ul >
< li > Agam Kumar (CMU)< / li >
< li > Yasaman S. Sefidgar (University of Washington)< / li >
< li > Joe Kim (Duke University)< / li >
< li > Brinnae Bent (Duke University)< / li >
< li > Stephen Price (CMU)< / li >
< li > Neil Singh (University of Virginia)< / li >
< / ul >
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< p > Many thanks to the researchers that made < a href = "./citation" > their work< / a > open source:< / p >
2021-07-02 00:33:38 +02:00
< ul >
< li > Panda et al. < a href = "https://pubmed.ncbi.nlm.nih.gov/31657854/" > paper< / a > < / li >
< li > Stachl et al. < a href = "https://www.pnas.org/content/117/30/17680" > paper< / a > < / li >
< li > Doryab et al. < a href = "https://arxiv.org/abs/1812.10394" > paper< / a > < / li >
< li > Barnett et al. < a href = "https://doi.org/10.1093/biostatistics/kxy059" > paper< / a > < / li >
< li > Canzian et al. < a href = "https://doi.org/10.1145/2750858.2805845" > paper< / a > < / li >
< / ul >
< / details >
< details class = "quote" > < summary > Publications using RAPIDS< / summary > < ul >
< li > Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data < a href = "https://www.frontiersin.org/articles/10.3389/fpsyt.2021.625247/full" > link< / a > < / li >
< li > Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyper-parameter Optimization, and Feature Importance Analysis: An Exploratory Study < a href = "https://preprints.jmir.org/preprint/26540" > link< / a > < / li >
< li > Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study < a href = "https://cancer.jmir.org/2021/2/e27975/" > link< / a > < / li >
< li > An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data < a href = "https://edas.info/showManuscript.php?m=1570708269&random=750318666&type=final&ext=pdf&title=PDF+file" > link< / a > < / li >
2021-07-13 00:24:07 +02:00
< li > Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity < a href = "https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract" > link< / a > < / li >
2021-07-02 00:33:38 +02:00
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