Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System
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. The authors 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. The authors' 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, they 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, they 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, they 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%.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/26877813
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Supplemental Notes:
- © 2024 The Authors.
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Authors:
- Alizadeh, Roya
- Savaria, Yvon
- Nerguizian, Chahé
- Publication Date: 2024
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 55-69
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Serial:
- IEEE Open Journal of Intelligent Transportation Systems
- Volume: 5
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2687-7813
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Classification; Decision trees; Human factors; Public transit
- Identifier Terms: WiFi services
- Subject Areas: Data and Information Technology; Public Transportation; Safety and Human Factors;
Filing Info
- Accession Number: 01916067
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 19 2024 9:48AM