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<h1 id="phone-accelerometer">Phone Accelerometer<a class="headerlink" href="#phone-accelerometer" title="Permanent link">&para;</a></h1>
<p>Sensor parameters description for <code>[PHONE_ACCELEROMETER]</code>:</p>
<table>
<thead>
<tr>
<th>Key&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>[CONTAINER]</code></td>
<td>Data stream <a href="../../datastreams/data-streams-introduction/">container</a> (database table, CSV file, etc.) where the accelerometer data is stored</td>
</tr>
</tbody>
</table>
<h2 id="rapids-provider">RAPIDS provider<a class="headerlink" href="#rapids-provider" title="Permanent link">&para;</a></h2>
<div class="admonition info">
<p class="admonition-title">Available time segments and platforms</p>
<ul>
<li>Available for all time segments</li>
<li>Available for Android and iOS</li>
</ul>
</div>
<div class="admonition info">
<p class="admonition-title">File Sequence</p>
<div class="highlight"><pre><span></span><code>- data/raw/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_raw.csv
- data/raw/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_with_datetime.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_features/phone_accelerometer_<span class="o">{</span>language<span class="o">}</span>_<span class="o">{</span>provider_key<span class="o">}</span>.csv
- data/processed/features/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer.csv
</code></pre></div>
</div>
<p>Parameters description for <code>[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]</code>:</p>
<table>
<thead>
<tr>
<th>Key&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>[COMPUTE]</code></td>
<td>Set to <code>True</code> to extract <code>PHONE_ACCELEROMETER</code> features from the <code>RAPIDS</code> provider</td>
</tr>
<tr>
<td><code>[FEATURES]</code></td>
<td>Features to be computed, see table below</td>
</tr>
</tbody>
</table>
<p>Features description for <code>[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]</code>:</p>
<table>
<thead>
<tr>
<th>Feature</th>
<th>Units</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>maxmagnitude</td>
<td>m/s<sup>2</sup></td>
<td>The maximum magnitude of acceleration (<span class="arithmatex">\(\|acceleration\| = \sqrt{x^2 + y^2 + z^2}\)</span>).</td>
</tr>
<tr>
<td>minmagnitude</td>
<td>m/s<sup>2</sup></td>
<td>The minimum magnitude of acceleration.</td>
</tr>
<tr>
<td>avgmagnitude</td>
<td>m/s<sup>2</sup></td>
<td>The average magnitude of acceleration.</td>
</tr>
<tr>
<td>medianmagnitude</td>
<td>m/s<sup>2</sup></td>
<td>The median magnitude of acceleration.</td>
</tr>
<tr>
<td>stdmagnitude</td>
<td>m/s<sup>2</sup></td>
<td>The standard deviation of acceleration.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Assumptions/Observations</p>
<ol>
<li>Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem.</li>
</ol>
</div>
<h2 id="panda-provider">PANDA provider<a class="headerlink" href="#panda-provider" title="Permanent link">&para;</a></h2>
<p>These features are based on the work by <a href="../../citation#panda-accelerometer">Panda et al</a>.</p>
<div class="admonition info">
<p class="admonition-title">Available time segments and platforms</p>
<ul>
<li>Available for all time segments</li>
<li>Available for Android and iOS</li>
</ul>
</div>
<div class="admonition info">
<p class="admonition-title">File Sequence</p>
<div class="highlight"><pre><span></span><code>- data/raw/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_raw.csv
- data/raw/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_with_datetime.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer_features/phone_accelerometer_<span class="o">{</span>language<span class="o">}</span>_<span class="o">{</span>provider_key<span class="o">}</span>.csv
- data/processed/features/<span class="o">{</span>pid<span class="o">}</span>/phone_accelerometer.csv
</code></pre></div>
</div>
<p>Parameters description for <code>[PHONE_ACCELEROMETER][PROVIDERS][PANDA]</code>:</p>
<table>
<thead>
<tr>
<th>Key&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>[COMPUTE]</code></td>
<td>Set to <code>True</code> to extract <code>PHONE_ACCELEROMETER</code> features from the <code>PANDA</code> provider</td>
</tr>
<tr>
<td><code>[FEATURES]</code></td>
<td>Features to be computed for exertional and non-exertional activity episodes, see table below</td>
</tr>
</tbody>
</table>
<p>Features description for <code>[PHONE_ACCELEROMETER][PROVIDERS][PANDA]</code>:</p>
<table>
<thead>
<tr>
<th>Feature</th>
<th>Units</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>sumduration</td>
<td>minutes</td>
<td>Total duration of all exertional or non-exertional activity episodes.</td>
</tr>
<tr>
<td>maxduration</td>
<td>minutes</td>
<td>Longest duration of any exertional or non-exertional activity episode.</td>
</tr>
<tr>
<td>minduration</td>
<td>minutes</td>
<td>Shortest duration of any exertional or non-exertional activity episode.</td>
</tr>
<tr>
<td>avgduration</td>
<td>minutes</td>
<td>Average duration of any exertional or non-exertional activity episode.</td>
</tr>
<tr>
<td>medianduration</td>
<td>minutes</td>
<td>Median duration of any exertional or non-exertional activity episode.</td>
</tr>
<tr>
<td>stdduration</td>
<td>minutes</td>
<td>Standard deviation of the duration of all exertional or non-exertional activity episodes.</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Assumptions/Observations</p>
<ol>
<li>Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem.</li>
<li>See <a href="../../citation#panda-accelerometer">Panda et al</a> for a definition of exertional and non-exertional activity episodes</li>
</ol>
</div>
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