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Setup
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Overview
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Minimal Example
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Execution
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Data Streams
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Data Streams
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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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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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</li>
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Phone Calls
</a>
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<a href="features/phone-conversation/" class="md-nav__link">
Phone Conversation
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</li>
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<a href="features/phone-data-yield/" class="md-nav__link">
Phone Data Yield
</a>
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<a href="features/phone-keyboard/" class="md-nav__link">
Phone Keyboard
</a>
</li>
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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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</li>
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<a href="features/phone-wifi-connected/" class="md-nav__link">
Phone WiFI Connected
</a>
</li>
<li class="md-nav__item">
<a href="features/phone-wifi-visible/" class="md-nav__link">
Phone WiFI Visible
</a>
</li>
</ul>
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Fitbit
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</label>
<nav class="md-nav" aria-label="Fitbit" data-md-level="2">
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Fitbit
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<li class="md-nav__item">
<a href="features/fitbit-data-yield/" class="md-nav__link">
Fitbit Data Yield
</a>
</li>
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<a href="features/fitbit-heartrate-summary/" class="md-nav__link">
Fitbit Heart Rate Summary
</a>
</li>
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<a href="features/fitbit-heartrate-intraday/" class="md-nav__link">
Fitbit Heart Rate Intraday
</a>
</li>
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Fitbit Sleep Summary
</a>
</li>
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<a href="features/fitbit-sleep-intraday/" class="md-nav__link">
Fitbit Sleep Intraday
</a>
</li>
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<a href="features/fitbit-steps-summary/" class="md-nav__link">
Fitbit Steps Summary
</a>
</li>
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<a href="features/fitbit-steps-intraday/" class="md-nav__link">
Fitbit Steps Intraday
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Empatica
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Empatica
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<a href="features/empatica-accelerometer/" class="md-nav__link">
Empatica Accelerometer
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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, modular, tested, and reproducible. At the moment, we support <a href="datastreams/data-streams-introduction">data streams</a> logged by smartphones, Fitbit wearables, and, in collaboration with the <a href="https://dbdp.org/">DBDP</a>, Empatica wearables (but you can <a href="datastreams/add-new-data-streams">add your own</a> too). </p>
<p><strong>If you want to know more head over to <a href="setup/overview/">Overview</a></strong></p>
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<p class="admonition-title">Tip</p>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M6 15a2 2 0 0 1-2 2 2 2 0 0 1-2-2 2 2 0 0 1 2-2h2v2m1 0a2 2 0 0 1 2-2 2 2 0 0 1 2 2v5a2 2 0 0 1-2 2 2 2 0 0 1-2-2v-5m2-8a2 2 0 0 1-2-2 2 2 0 0 1 2-2 2 2 0 0 1 2 2v2H9m0 1a2 2 0 0 1 2 2 2 2 0 0 1-2 2H4a2 2 0 0 1-2-2 2 2 0 0 1 2-2h5m8 2a2 2 0 0 1 2-2 2 2 0 0 1 2 2 2 2 0 0 1-2 2h-2v-2m-1 0a2 2 0 0 1-2 2 2 2 0 0 1-2-2V5a2 2 0 0 1 2-2 2 2 0 0 1 2 2v5m-2 8a2 2 0 0 1 2 2 2 2 0 0 1-2 2 2 2 0 0 1-2-2v-2h2m0-1a2 2 0 0 1-2-2 2 2 0 0 1 2-2h5a2 2 0 0 1 2 2 2 2 0 0 1-2 2h-5z"/></svg></span> Questions or feedback can be posted on the #rapids channel in AWARE Framework's <a href="http://awareframework.com:3000/">slack</a>. </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 and feature requests should be posted on <a href="https://github.com/carissalow/rapids/issues">Github</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> Join our discussions on our algorithms and assumptions for feature <a href="https://github.com/carissalow/rapids/discussions">processing</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>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M424.4 214.7 72.4 6.6C43.8-10.3 0 6.1 0 47.9V464c0 37.5 40.7 60.1 72.4 41.3l352-208c31.4-18.5 31.5-64.1 0-82.6z"/></svg></span> Ready? Go to <a href="setup/overview/">Overview</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 Snakemake, 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>Timezone 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>
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