Wi-HFM: A Human Flow Monitoring Method Based on WiFi Channel Features

Yang Zhiyong; Lu Chao; Wang Junjie

doi:10.7544/issn1000-1239.202330712

Journal of Computer Research and Development > 2025 > 62(3): 720-732. > DOI: 10.7544/issn1000-1239.202330712 CSTR: 32373.14.issn1000-1239.202330712

Yang Zhiyong, Lu Chao, Wang Junjie. Wi-HFM: A Human Flow Monitoring Method Based on WiFi Channel Features[J]. Journal of Computer Research and Development, 2025, 62(3): 720-732. DOI: 10.7544/issn1000-1239.202330712

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Wi-HFM: A Human Flow Monitoring Method Based on WiFi Channel Features

School of Software, Nanchang Hangkong University, Nanchang 330063

Funds: This work was supported by the National Natural Science Foundation of China for Young Scientists (61501218) and the Natural Science Foundation of Jiangxi Province (20181BAB202015).

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Author Bio:
Yang Zhiyong: born in 1982. PhD, associate professor. Member of CCF. His main research interests include wireless sensing, artificial intelligence, and Internet of things

Lu Chao: born in 2000. Master. His main research interests include wireless sensing and machine learning

Wang Junjie: born in 1996. Master. His main research interests include wireless sensing and machine learning
Received Date: August 30, 2023
Revised Date: June 23, 2024
Accepted Date: August 08, 2024
Available Online: August 14, 2024

Graphical Abstract

Abstract

Abstract

With the increasing demand for people counting, the technology of human flow monitoring based on channel state information (CSI) attracts much attention because of its advantages such as easy deployment, privacy protection and strong applicability. However, in the existing human flow monitoring work, the accuracy of pedestrian recognition is easily affected by the density of the crowd. To ensure the monitoring accuracy, the monitoring can only be carried out when the crowd is sparse, which leads to the lack of practicability of the human flow monitoring technology based on CSI. In order to solve this problem, a monitoring method that can identify continuous flow of people is proposed. The method firstly uses phase unwrapping and linear phase correction algorithm to eliminate random phase offset and phase compensation for original data, then extracts valid data packets from continuous flow data by standard deviation and variance, and finally inputs phase difference information in the time domain as feature signals into the deep learning convolutional, long short-term memory, deep neural network (CLDNN) for pedestrian recognition. After actual testing, the method achieves outdoor accuracy of 96.7% and indoor accuracy of 94.1% under the condition that the distance between pedestrians in front and back is not less than 1 m, outperforming the existing method of human flow monitoring.
- channel state information (CSI),
- human flow monitoring,
- continuous human flow,
- phase correction,
- deep learning

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References (30)

References

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