A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments.

ABSTRACT: 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

SUBMITTER: Alsaify BA 

PROVIDER: S-EPMC7704290 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature