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A dataset for Wi-Fi-based human-to-human interaction recognition.

Rami Alazrai¹, Ali Awad¹, Baha'A Alsaify²

¹Department of Computer Engineering, German Jordanian University, P.O. Box 35247, Amman 11180, Jordan.

|May 29, 2020

Summary

This summary is machine-generated.

This study introduces a new dataset for recognizing human-to-human interactions using Wi-Fi signals. It includes 4,800 trials of 12 interactions performed by 40 pairs, advancing Wi-Fi-based activity recognition.

Keywords:

Channel State Information (CSI)Human Activity Recognition Received Signal Strength Indicator (RSSI)Two-Person Interaction Wi-Fi

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Area of Science:

Computer Science
Signal Processing
Human-Computer Interaction

Background:

Existing Wi-Fi-based human activity datasets primarily focus on single-person activities.
There is a need for comprehensive datasets to study complex human-to-human interactions using wireless signals.

Purpose of the Study:

To present a novel dataset for Wi-Fi-based human-to-human interaction recognition.
To facilitate research in recognizing complex interactions between multiple individuals using wireless signals.

Main Methods:

Collected 4,800 Wi-Fi signal trials (RSSI and CSI) from 40 pairs performing 12 distinct interactions.
Utilized a commercial access point (Sagemcom 2704) and an Intel 5300 NIC with the CSI tool.
Focused on two-person interactions in an indoor environment.

Main Results:

A dataset comprising 4,800 trials of 12 different human-to-human interactions.
Data includes Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) values.
The dataset specifically captures interactions between pairs of subjects.

Conclusions:

The presented dataset is valuable for advancing Wi-Fi-based human activity recognition, particularly for multi-person interactions.
It enables the exploration of various machine learning algorithms for recognizing complex human-to-human behaviors.
This resource supports future research in ubiquitous sensing and human-computer interaction.