Introduction

These datasets are collected by Atheros CSI Tool and used in our teams's publications. There are 2 datasets in total.
Dataset1 is for paper “Domain-Agnostic Sample-Effcient Wireless Indoor Crowd Counting via Few-shot Learning”.
Dataset2 is for paper “ResMon: Domain-adaptive Wireless Respiration State Monitoring via Few-shot Bayesian Deep Learning”.

Dataset1: DASECount

Formats

This dataset is for paper “Domain-Agnostic Sample-Effcient Wireless Indoor Crowd Counting via Few-shot Learning”, which has been submitted for potential journal publication. The conference version is available in IEEE WOCC 2022 . The dataset contains 9 “pickle” file in total. Each file contains split CSI amplitude data, phase difference data and labels. Data collection scenarios are shown as follows:

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Each scenario contains three crowd motion types data: Static, Dynamic and Mixed.
Static: volunteers are required to remain seated but can act freely, such as eating, typing, or sleeping;
Dynamic: volunteers walk randomly walk around the venue;
Mixed: there is no restriction to the volunteers’ activities, and they can move freely in the venue including but not limited to walking, sitting, eating, and sleeping.

Each type data contains a crowd range of 0-8 people. And each class contains 600 samples.

Download Links

Usage in Python

import pickle
import numpy
with open(“office_los_static.pickle”, “rb”) as file:
        dataset = pickle.load(file)
        csi_amp = dataset[“abs”]
        csi_pha = dataset[“phase_df”]
        csi_label = dataset[“labels”]

“abs”: original amplitude data from split CSI stream.
“phase_df”: phase difference data, by computing difference between two adjacent receiving antennas.
“labels”: label of data, is the same as the number of people in the scene.

Dataset2: ResMon

Formats

This dataset is for paper “ResMon: Domain-adaptive Wireless Respiration State Monitoring via Few-shot Bayesian Deep Learning”, which has been submitted for potential journal publication. The dataset contains 6 “zip” files in total. Each file contains 4 respiration state classes (i.e. stable breath, cough, sneeze, and yawn) of CSI sample files. These sample are collected in two indoor environments, i.e., a lab and a meeting room, containing one source domain area and two target domain areas, as shown in the follows:

alt text 

The source domain dataset totally consists of 720 samples for volunteer A, B and C. The target domain dataset 1 and 2 respectively consists of 200 samples for volunteer A to F. In additions, 5 time serial samples and the subdatasets collected under different distances, operating frequency band are also included in target domain 1.

Download Links

Usage in Python

import numpy as np
data=np.load(’S_COUGH.npy’)

A subdataset under “cough” label in source domain