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Characterization and selection of WiFi channel state information features for human activity detection in a smart public transportation system

Roya Alizadeh, Yvon Savaria and Chahe Nerguizian

Article (2024)

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Abstract

Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision = 83%, True Positive (TP)=94% , True Negative (TN)=91% and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, TP=97% , TN=95% and F1-score = 95%.

Uncontrolled Keywords

feature extraction and analysis; classification; human activity recognition; channel state information (CSI); chirp rate; smart public transportation systems; principal component analysis (PCA); decision tree; feature selection

Subjects: 1000 Civil engineering > 1003 Transportation engineering
2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
2500 Electrical and electronic engineering > 2506 Electronic circuits and devices
Department: Department of Electrical Engineering
Funders: CRSNG/NSERC
PolyPublie URL: https://publications.polymtl.ca/57261/
Journal Title: IEEE Open Journal of Intelligent Transportation Systems (vol. 5)
Publisher: IEEE
DOI: 10.1109/ojits.2023.3336795
Official URL: https://doi.org/10.1109/ojits.2023.3336795
Date Deposited: 29 Jan 2024 14:38
Last Modified: 21 Apr 2024 22:43
Cite in APA 7: Alizadeh, R., Savaria, Y., & Nerguizian, C. (2024). Characterization and selection of WiFi channel state information features for human activity detection in a smart public transportation system. IEEE Open Journal of Intelligent Transportation Systems, 5, 55-69. https://doi.org/10.1109/ojits.2023.3336795

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