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. DOI:https://doi.org/10.1145/3307334.3326081Yi Zhang, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, Zheng Yang, "GaitID: Robust Wi-Fi Based Gait Recognition", Springer WASA, 2020. DOI:https://doi.org/10.1007/978-3-030-59016-1_60