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

为促进无线感知技术的发展，我们面向科研社区发布了这套基于Wi-Fi的行为识别数据集。本数据集包含两类核心数据：一是由商用Wi-Fi设备采集的、用于手势识别的信道状态信息（Channel State Information，CSI）；二是基于Widar3.0论文所载算法计算得到的身体坐标速度剖面（Body-coordinate Velocity Profile，BVP）。该手势数据集共计25.8万个数据样本，总时长达8620分钟，覆盖75个采集域。本仓库同时收录了与GaitID论文相关的步态识别数据集，该数据集包含11名受试者的2.2万个数据样本。敬请关注后续更新。参考文献：1. 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"[C]//ACM MobiSys, 2019. DOI: https://doi.org/10.1145/3307334.3326081；2. Yi Zhang, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, Zheng Yang. "GaitID: Robust Wi-Fi Based Gait Recognition"[C]//Springer WASA, 2020. DOI: https://doi.org/10.1007/978-3-030-59016-1_60

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，总时长为8,620分钟，覆盖75个域。数据集提供了RSSI和CSI形式的RF数据，以及DFS和BVP两种高级特征，适用于手势识别、步态识别等多种RF活动识别研究。