WIDAR3.0: 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的手势识别任务研发的大型数据集。该数据集的射频（RF）数据采集自商用WiFi网卡，格式为接收信号强度指示符（Received Signal Strength Indicator, RSSI）与信道状态信息（Channel State Information, CSI）。数据集包含25.8万个手势实例，总时长共计8620分钟，涵盖75个不同采集域。此外，数据集还提供了两种从原始射频信号中提取的精密特征：多普勒频移（Doppler Frequency Shift, DFS）以及全新特征体坐标速度剖面（Body-coordinate Velocity Profile, BVP）。未来还将发布更多基于射频的活动识别数据（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http://tns.thss.tsinghua.edu.cn/widar3.0/。引用该数据集的最优参考文献为2019年ACM MobiSys会议收录的论文"Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi"。