Widar 3.0: WiFi-based Activity Recognition Dataset

The Widar3.0 project is a large dataset designed for use in WiFi-based hand gesture recognition. The RF data are collected from commodity WiFi NICs in the form of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The dataset consists of 258K instances of hand gestures with a duration of totally 8,620 minutes and from 75 domains. In addition, two sophisticated features from raw RF signal, including Doppler Frequency Shift (DFS) and a new feature Body-coordinate Velocity Profile (BVP) are included. More kinds of RF-based activity recognition data (e.g., gait identification, fall detection) are going to come. Please stay tuned for further updates.More details are available at http://tns.thss.tsinghua.edu.cn/widar3.0/. To cite this dataset, the best reference is the paper "Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi" in ACM MobiSys 2019.

Widar3.0项目是一款专为基于WiFi的手势识别任务设计的大型数据集。其射频（RF, Radio Frequency）数据采集自商用WiFi网卡，数据格式包含接收信号强度指示（RSSI, Received Signal Strength Indicator）与信道状态信息（CSI, Channel State Information）。该数据集共计25.8万个手势样本，总时长达8620分钟，涵盖75个不同域。此外，数据集还包含两种从原始射频信号中提取的进阶特征：多普勒频移（DFS, Doppler Frequency Shift），以及一项全新特征——身体坐标系速度剖面（BVP, Body-coordinate Velocity Profile）。未来还将推出更多基于射频的活动识别数据（例如步态识别、跌倒检测），敬请关注后续更新。更多详细信息可访问网址：http://tns.thss.tsinghua.edu.cn/widar3.0/。如需引用该数据集，最佳参考文献为ACM MobiSys 2019会议论文《Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi》。

搜集汇总

背景与挑战

背景概述

Widar 3.0是一个大规模WiFi手势识别数据集，包含258K个手势实例和75个领域的RF数据（RSSI/CSI），并提取了DFS和BVP高级特征。数据集总时长8620分钟，未来还将扩展步态识别等更多活动识别数据。