WIDAR3.0: WiFi-based Activity Recognition Dataset

To stimulate the development of wireless sensing, we produce this wifi-based activity recognition dataset to the community. This dataset includes the Channel State Information (CSI) collected from commodity Wi-Fi devices for gestures and Body-coordinate Velocity Profile (BVP) calculated by the algorithms descried in the Widar3.0 paper. The hand gesture dataset consists of 258K instances of data samples with a duration of 8,620 minutes and from 75 domains. Also included in this repository is the gait recognition dataset related to the GaitID paper. The gait recognition dataset consists of 22K instances of data samples from 11 participates. Please stay tuned for further updates. References:Yue Zheng, Yi Zhang, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, Zheng Yang "Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi", ACM MobiSys, 2019.Yi Zhang, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, Zheng Yang "GaitID: Robust Wi-Fi Based Gait Recognition", Springer WASA, 2020.为促进无线感知领域的发展，我们面向学术社区发布了这款基于Wi-Fi的行为识别数据集。本数据集包含从商用Wi-Fi设备采集得到的手势相关信道状态信息（Channel State Information, CSI），以及基于Widar3.0论文中所述算法计算得到的体坐标速度剖面（Body-coordinate Velocity Profile, BVP）数据。该手势识别数据集包含25.8万条数据样本，总时长达8620分钟，涵盖75个不同域。本数据集存储库中还收录了与GaitID论文相关的步态识别数据集。该步态识别数据集包含2.2万条数据样本，共涉及11名参与者。敬请期待后续更新。参考文献：Yue Zheng, Yi Zhang, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, Zheng Yang 《Widar3.0：基于Wi-Fi的零投入跨域手势识别》，ACM MobiSys, 2019. Yi Zhang, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, Zheng Yang 《GaitID：基于Wi-Fi的鲁棒步态识别》，Springer WASA, 2020.

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258,000个手势实例，总时长8,620分钟，覆盖75个域，数据来自商用WiFi网卡的RSSI和CSI信号，并提取了DFS和BVP等高级特征。该数据集专注于基于射频的活动识别研究，未来计划扩展至步态识别和跌倒检测等更多应用场景。