Widar3.0 Dataset: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi

This repository hosts the data and code for paper Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi published in ACM MobiSys 2019.This dataset includes the Channel State Information (CSI) data collected from commodity Wi-Fi devicesnbsp;for gesturesnbsp;and Body-coordinate Velocity Profile (BVP) datanbsp;calculated by the algorithms from the above mentioned paper.Also included in this repository is the data for paper GaitID: Robust WiFi Based Gait Recognition published in Springer WASA 2020.Please stay tuned for further updates.Due to technical issues, please access our dataset homepage for download links: http://tns.thss.tsinghua.edu.cn/widar3.0/

本仓库收录了发表于ACM MobiSys 2019的论文《Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi》（基于Wi-Fi的零工作量跨域手势识别）对应的数据集与代码。本数据集包含由商用Wi-Fi设备采集的手势相关信道状态信息（Channel State Information，CSI）数据，以及由上述论文提出的算法计算得到的身体坐标速度剖面（Body-coordinate Velocity Profile，BVP）数据。本仓库同时收录了发表于Springer WASA 2020的论文《GaitID: Robust WiFi Based Gait Recognition》（GaitID：基于Wi-Fi的鲁棒步态识别）对应的数据集。敬请期待后续更新。由于技术原因，请访问我们的数据集主页获取下载链接：http://tns.thss.tsinghua.edu.cn/widar3.0/

搜集汇总

背景与挑战

背景概述

Widar3.0是一个基于WiFi的手势识别大型数据集，包含258K个手势实例和8,620分钟的数据，覆盖75个域。数据集提供了RSSI和CSI形式的原始数据，以及DFS和BVP两种高级特征，并附有特征提取代码和预训练模型。