Alsaify Baha' A, Almazari Mahmoud M, Alazrai Rami, Daoud Mohammad I
Department of Network Engineering and Security, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Department of Computer Engineering, German Jordanian University, P.O. Box 35247, Amman 11180, Jordan.
Data Brief. 2020 Nov 18;33:106534. doi: 10.1016/j.dib.2020.106534. eCollection 2020 Dec.
The aim of this paper is to present a dataset for Wi-Fi-based human activity recognition. The dataset is comprised of five experiments performed by 30 different subjects in three different indoor environments. The experiments performed in the first two environments are of a line-of-sight (LOS) nature, while the experiments performed in the third environment are of a non-line-of-sight (NLOS) nature. Each subject performed 20 trials for each of the experiments which makes the overall number of recorded trials in the dataset equals to 3000 trials (30 subjects × 5 experiments × 20 trials). To record the data, we used the channel state information (CSI) tool [1] to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver. The utilized transmitter and receiver are retrofitted with the Intel 5300 network interface card which enabled us to capture the CSI values that are contained in the recorded transmissions. Unlike other publicly available human activity datasets, this dataset provides researchers with the ability to test their developed methodologies on both LOS and NLOS environments, in addition to many different variations of human movements, such as walking, falling, turning, and pen pick up from the ground.
本文的目的是呈现一个用于基于Wi-Fi的人类活动识别的数据集。该数据集由30名不同受试者在三种不同室内环境中进行的五项实验组成。在前两种环境中进行的实验具有视距（LOS）性质，而在第三种环境中进行的实验具有非视距（NLOS）性质。每个受试者对每个实验进行20次试验，这使得数据集中记录的试验总数等于3000次试验（30名受试者×5项实验×20次试验）。为了记录数据，我们使用信道状态信息（CSI）工具[1]来捕获Wi-Fi发射器和接收器之间交换的Wi-Fi数据包。所使用的发射器和接收器配备了英特尔5300网络接口卡，这使我们能够捕获记录传输中包含的CSI值。与其他公开可用的人类活动数据集不同，该数据集为研究人员提供了在视距和非视距环境中测试其开发方法的能力，此外还包括许多不同的人类运动变化，如行走、跌倒、转身以及从地面捡起笔等。
