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Phone Data Quality
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Phone Accelerometer
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Phone Activity Recognition
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What are the benefits of using RAPIDS?
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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 extract <strong>behavioral features</strong> (a.k.a. digital biomarkers/phenotypes).</p>
<p>RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with <a href="https://awareframework.com/">AWARE</a> and wearable data from Fitbit devices.</p>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M6 15a2 2 0 01-2 2 2 2 0 01-2-2 2 2 0 012-2h2v2m1 0a2 2 0 012-2 2 2 0 012 2v5a2 2 0 01-2 2 2 2 0 01-2-2v-5m2-8a2 2 0 01-2-2 2 2 0 012-2 2 2 0 012 2v2H9m0 1a2 2 0 012 2 2 2 0 01-2 2H4a2 2 0 01-2-2 2 2 0 012-2h5m8 2a2 2 0 012-2 2 2 0 012 2 2 2 0 01-2 2h-2v-2m-1 0a2 2 0 01-2 2 2 2 0 01-2-2V5a2 2 0 012-2 2 2 0 012 2v5m-2 8a2 2 0 012 2 2 2 0 01-2 2 2 2 0 01-2-2v-2h2m0-1a2 2 0 01-2-2 2 2 0 012-2h5a2 2 0 012 2 2 2 0 01-2 2h-5z"/></svg></span> Questions or feedback can be posted on #rapids 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 0 002 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 0012 2z"/></svg></span> Bugs should be reported 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 00-17 0L58.93 98.81l-22.7-22.12a12 12 0 00-17 0L3.53 92.41a12 12 0 000 17l47.59 47.4a12.78 12.78 0 0017.61 0l15.59-15.62L156.52 69a12.09 12.09 0 00.09-17zm0 159.19a12 12 0 00-17 0l-63.68 63.72-22.7-22.1a12 12 0 00-17 0L3.53 252a12 12 0 000 17L51 316.5a12.77 12.77 0 0017.6 0l15.7-15.69 72.2-72.22a12 12 0 00.09-16.9zM64 368c-26.49 0-48.59 21.5-48.59 48S37.53 464 64 464a48 48 0 000-96zm432 16H208a16 16 0 00-16 16v32a16 16 0 0016 16h288a16 16 0 0016-16v-32a16 16 0 00-16-16zm0-320H208a16 16 0 00-16 16v32a16 16 0 0016 16h288a16 16 0 0016-16V80a16 16 0 00-16-16zm0 160H208a16 16 0 00-16 16v32a16 16 0 0016 16h288a16 16 0 0016-16v-32a16 16 0 00-16-16z"/></svg></span> Join our discussions on our algorithms and assumptions for feature <a href="https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion">processing</a>.</p>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path d="M424.4 214.7L72.4 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 to start? Go to <a href="setup/installation/">Installation</a> and then to <a href="setup/configuration/">Initial Configuration</a></p>
<h2 id="how-does-it-work">How does it work?<a class="headerlink" href="#how-does-it-work" title="Permanent link">&para;</a></h2>
<p>RAPIDS is formed by R and Python scripts orchestrated by <a href="https://snakemake.readthedocs.io/en/stable/">Snakemake</a>. We suggest you read Snakemake&rsquo;s docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant.</p>
<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 exact 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>Extensible code</strong>. You can easily add your own behavioral features in R or Python and keep authorship and citations.</li>
<li><strong>Timezone aware</strong>. Your data is adjusted to the specified timezone (multiple timezones suport <em>coming soon</em>).</li>
<li><strong>Flexible day 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 constantly adding tests to make sure our behavioral features are correct.</li>
<li><strong>Reproducible code</strong>. 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 your publications without any overhead.</li>
<li><strong>Private</strong>. All your data is processed locally.</li>
</ol>
<h2 id="how-is-it-organized">How is it organized?<a class="headerlink" href="#how-is-it-organized" title="Permanent link">&para;</a></h2>
<p>In broad terms the <code>config.yaml</code>, <code>.env</code> files, participant files, and day segment files are the only ones that you will have to modify. All data is stored in <code>data/</code> and all scripts are stored in <code>src/</code>. For more information see RAPIDS&rsquo; <a href="file-structure/">File Structure</a>.</p>
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