WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32
<p><strong>WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32</strong></p>
<p>This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1].</p>
<p><strong>Datasets</strong></p>
<ul>
<li><strong>DP_LOS</strong> - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms.</li>
<li><strong>DP_NLOS </strong>- Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms.</li>
<li><strong>DA_LOS</strong> - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms.</li>
<li><strong>DA_NLOS</strong> - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms.</li>
</ul>
<p>Table 1: Characteristics of presence detection and activity recognition datasets. </p>
<table>
<tbody>
<tr>
<td><strong>Dataset</strong></td>
<td><strong>Scenario</strong></td>
<td><strong>#Rooms</strong></td>
<td><strong>#Persons</strong></td>
<td><strong>#Classes</strong></td>
<td><strong>Packet Sending Rate</strong></td>
<td><strong>Interval </strong></td>
<td><strong>#Spectrograms</strong></td>
</tr>
<tr>
<td>DP_LOS</td>
<td>LOS</td>
<td>1</td>
<td>1</td>
<td>6</td>
<td>100Hz</td>
<td>4s (400 packets)</td>
<td>392</td>
</tr>
<tr>
<td>DP_NLOS</td>
<td>NLOS</td>
<td>5</td>
<td>1</td>
<td>6</td>
<td>100Hz</td>
<td>4s (400 packets)</td>
<td>384</td>
</tr>
<tr>
<td>DA_LOS</td>
<td>LOS</td>
<td>1</td>
<td>1</td>
<td>3</td>
<td>100Hz</td>
<td>4s (400 packets)</td>
<td>392</td>
</tr>
<tr>
<td>DA_NLOS</td>
<td>NLOS</td>
<td>5</td>
<td>1</td>
<td>3</td>
<td>100Hz</td>
<td>4s (400 packets)</td>
<td>384</td>
</tr>
</tbody>
</table>
<p> </p>
<p><strong>Data Format</strong></p>
<p>Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files <em>trainLabels.csv</em>, <em>validationLabels.csv</em>, and <em>testLabels.csv</em>. Label files use the sample format [<em>i c</em>], with <em>i</em> corresponding to the spectrogram index (i.png) and <em>c </em>corresponding to the class. For presence detection datasets (DP_LOS <em>, </em>DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS <em>, </em>DA_NLOS), c in {0="no activity", 1="walking", and 2="walking + arm-waving"}. Furthermore, the mean and standard deviation of a given dataset are provided in <em>meanStd.csv</em>.</p>
<p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p>
<p>[1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" <em>International Conference on Computer Vision Systems</em>. Cham: Springer Nature Switzerland, 2023. </p>
<p>BibTeX citation:</p>
<pre>@inproceedings{strohmayer2023wifi,
title={WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32},
author={Strohmayer, Julian and Kampel, Martin},
booktitle={International Conference on Computer Vision Systems},
pages={41--50},
year={2023},
organization={Springer}
}</pre>
opencc-by-4.0Sep 2023View details →
