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2021-08-10 00:23:31 +02:00
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What are the benefits of using RAPIDS?
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Minimal Example
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Configuration
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Execution
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Data Streams
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Data Streams
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Introduction
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Phone
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aware_mysql
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aware_csv
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aware_influxdb (beta)
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Mandatory Phone Format
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Fitbit
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fitbitjson_mysql
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fitbitjson_csv
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fitbitparsed_mysql
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fitbitparsed_csv
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Mandatory Fitbit Format
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empatica_zip
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Mandatory Empatica Format
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Add New Data Streams
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Behavioral Features
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Behavioral Features
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Introduction
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Phone
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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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Phone Screen
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Phone WiFI Connected
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Phone WiFI Visible
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Fitbit
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Fitbit
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<a href="features/fitbit-calories-intraday/" class="md-nav__link">
Fitbit Calories Intraday
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Fitbit Data Yield
</a>
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Fitbit Heart Rate Summary
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Fitbit Heart Rate Intraday
</a>
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Fitbit Sleep Summary
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<a href="features/fitbit-sleep-intraday/" class="md-nav__link">
Fitbit Sleep Intraday
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Fitbit Steps Summary
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Fitbit Steps Intraday
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<h1 id="welcome-to-rapids-documentation">Welcome to RAPIDS documentation<a class="headerlink" href="#welcome-to-rapids-documentation" title="Permanent link">&para;</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>
<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>
<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>
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<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">&para;</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>
<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>
<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">&para;</a></h2>
<details class="quote"><summary>Community Contributors</summary><p>Many thanks to the <a href="./team">whole team</a> and our community contributions:</p>
<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>
<p>Many thanks to the researchers that made <a href="./citation">their work</a> open source:</p>
<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, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study <a href="https://mhealth.jmir.org/2021/7/e26540">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://ieeexplore.ieee.org/abstract/document/9483755/">link</a></li>
<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>
</ul>
</details>
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