WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32

WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32 This repository contains the WiFi CSI human presence detection and activity recognition datasets proposed in [1]. Datasets DP_LOS - Line-of-sight (LOS) presence detection dataset, comprised of 392 CSI amplitude spectrograms. DP_NLOS - Non-line-of-sight (NLOS) presence detection dataset, comprised of 384 CSI amplitude spectrograms. DA_LOS - LOS activity recognition dataset, comprised of 392 CSI amplitude spectrograms. DA_NLOS - NLOS activity recognition dataset, comprised of 384 CSI amplitude spectrograms. Table 1: Characteristics of presence detection and activity recognition datasets. Dataset Scenario #Rooms #Persons #Classes Packet Sending Rate Interval #Spectrograms DP_LOS LOS 1 1 6 100Hz 4s (400 packets) 392 DP_NLOS NLOS 5 1 6 100Hz 4s (400 packets) 384 DA_LOS LOS 1 1 3 100Hz 4s (400 packets) 392 DA_NLOS NLOS 5 1 3 100Hz 4s (400 packets) 384 Data Format Each dataset employs an 8:1:1 training-validation-test split, defined in the provided label files trainLabels.csv, validationLabels.csv, and testLabels.csv. Label files use the sample format [i c], with i corresponding to the spectrogram index (i.png) and c corresponding to the class. For presence detection datasets (DP_LOS , DP_NLOS), c in {0 = "no presence", 1 = "presence in room 1", ..., 5 = "presence in room 5"}. For activity recognition datasets (DA_LOS , 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 meanStd.csv. Download and Use 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]. [1] Strohmayer, Julian, and Martin Kampel. "WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32" International Conference on Computer Vision Systems. Cham: Springer Nature Switzerland, 2023. BibTeX citation: @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}
}

基于WiFi信道状态信息（Channel State Information, CSI）的ESP32远程穿墙人体活动识别数据集

本仓库包含文献[1]中提出的基于WiFi CSI的人体存在检测与活动识别数据集。

DP_LOS：视距（Line-of-sight, LOS）人体存在检测数据集，包含392张CSI幅度频谱图。
DP_NLOS：非视距（Non-line-of-sight, NLOS）人体存在检测数据集，包含384张CSI幅度频谱图。
DA_LOS：视距人体活动识别数据集，包含392张CSI幅度频谱图。
DA_NLOS：非视距人体活动识别数据集，包含384张CSI幅度频谱图。

表1：人体存在检测与活动识别数据集参数特征
| 数据集名称 | 部署场景 | 房间数量 | 参与人数 | 类别总数 | 数据包发送速率 | 采集间隔 | 频谱图数量 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| DP_LOS | LOS | 1 | 1 | 6 | 100Hz | 4s（含400个数据包） | 392 |
| DP_NLOS | NLOS | 5 | 1 | 6 | 100Hz | 4s（含400个数据包） | 384 |
| DA_LOS | LOS | 1 | 1 | 3 | 100Hz | 4s（含400个数据包） | 392 |
| DA_NLOS | NLOS | 5 | 1 | 3 | 100Hz | 4s（含400个数据包） | 384 |

数据格式
所有数据集均采用8:1:1的训练集-验证集-测试集划分比例，划分规则已在提供的标签文件trainLabels.csv、validationLabels.csv和testLabels.csv中定义。标签文件采用格式为[i, c]的样本结构，其中i对应频谱图的文件名（即i.png），c对应类别标签。

对于人体存在检测数据集（DP_LOS、DP_NLOS），c的取值范围为{0="无人体存在", 1="1号房间内有人", …, 5="5号房间内有人"}。
对于人体活动识别数据集（DA_LOS、DA_NLOS），c的取值范围为{0="无活动", 1="行走", 2="行走+挥臂"}。

此外，各数据集的均值与标准差已存储于meanStd.csv文件中。

下载与使用
本数据集仅可用于非商业性研究用途。若您基于本数据集发表研究成果，请务必引用我们的文献[1]。

[1] Strohmayer, Julian, Martin Kampel. 基于WiFi CSI的ESP32远程穿墙人体活动识别[C]//International Conference on Computer Vision Systems. Cham: Springer Nature Switzerland, 2023: 41-50.

BibTeX引用格式：
@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}
}