Widar 3.0: WiFi-based Activity Recognition Dataset

Citation Author(s):
Zheng
Yang
School of Software, Tsinghua University
Yi
Zhang
School of Software, Tsinghua University
Guidong
Zhang
School of Software, Tsinghua University
Yue
Zheng
School of Software, Tsinghua University
Submitted by:
ZHENG YANG
Last updated:
Thu, 01/14/2021 - 21:31
DOI:
10.21227/7znf-qp86
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Abstract 

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.

Instructions: 

Please refer to the README document.

Comments

Research

Submitted by Nayan Bhatia on Thu, 02/18/2021 - 02:42

Dataset Files

Documentation

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File README_IEEE_DataPort.pdf392.12 KB