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A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation

  • 1229: Multimedia Data Analysis for Smart City Environment Safety
  • Published:
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Abstract

Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. Finally, this article concludes with a discussion of the possible solutions to the observed limitations and identifies areas for further research. Such a Wi-Fi channel perturbation pattern analysis is believed to be an efficient, economical, and privacy-friendly approach to be potentially utilized in mutual human interaction recognition for indoor activity monitoring, surveillance system, smart health monitoring systems, and independent assisted living.

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Data Availability Statement

The dataset [32] analyzed during the current study is available in the Mendeley Data Repository, https://data.mendeley.com/datasets/3dhn4xnjxw/1.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Islamic University of Technology, Boardbazar, Gazipur, 1704, Bangladesh

    Md Mohi Uddin Khan

  2. The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, M5S 3G4, Ontario, Canada

    Abdullah Bin Shams

  3. Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh

    Mohsin Sarker Raihan

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Md. Mohi Uddin Khan: Idea generation, Methodology, Formal analysis, Research validation, Visualization, Software, Writing - Original Draft, Editing, Revision, and Quality control. Abdullah Bin Shams and Md. Mohsin Sarker Raihan: Idea generation and Supervision. Finally all authors reviewed and discussed on the article, provided critical feedback and contributed to the final manuscript.

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Correspondence to Md Mohi Uddin Khan.

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Appendix A: MIMO-OFDM

Appendix A: MIMO-OFDM

With the advent of high-speed wireless local area network (WLAN) communication techniques, a multi-antenna system called Multiple Input Multiple Output (MIMO) (Fig. 7(a)) has increased the throughput, spectral efficiency, quality-of-service (QoS) as well as transmission range by means of signal fading reduction after utilizing the Orthogonal Frequency Division Multiplexing (OFDM) (Fig. 7(b)) transmission scheme along with IEEE 802.11n amendment compared to IEEE 802.11a/b/g standards ensuring the use of 2.4 GHz carrier frequency band and 20 MHz (optional 40 MHz) channel bandwidth which brought down the production cost and throughput maximized to 600 Mbps [51].

Fig. 7
figure 7

(a) Generalized block diagram of 2x3 MIMO-OFDM transmission and reception structures according to IEEE 802.11n standard (b) OFDM Intuition using three subcarriers

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Previously, for Frequency Division Multiplexing (FDM), the overall channel bandwidth had to be equally divided for N subcarriers with the addition of a guard interval between two subcarriers. But, the invention of calculation-efficient Discrete Fast Fourier Transform (DFFT) using matrix-algebra, affordable high-speed VLSI circuit design have geared up the development of OFDM modulation scheme.

The analog baseband signal is converted to digital (with an A/D converter) followed by symbol creation via constellation mapper. The overall channel bandwidth is divided into N (51 in this experiment, note that Intel NIC can report only 30) non-overlapping subcarrier bandwidth in such a way that subcarrier-spacing \(\left( \Delta f=\frac{1}{T} \right) \) is minimum and respective signals are orthogonal to each other. Then the symbols are modulated with a particular subcarrier frequency. After addition of a cyclic prefix for taking care of the Inter-Symbol-Interference (ISI) and other processing, the frequency domain signal is converted to a time domain analog signal using Inverse-FFT for radio wave transmission generated by the transmitter oscillator. The orthogonal property of the subcarriers prevents the Inter-Carrier-Interference. The symbol rate for each subcarrier needs to be less so as to reduce aliasing and multipath propagation effects but that is compensated by an increased number of orthogonal subcarriers which all in all increased the net transmission rate (throughput) via efficient use of available channel spectrum [55, 71, 94].

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Khan, M.M.U., Shams, A.B. & Raihan, M.S. A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation. Multimed Tools Appl 83, 62379–62422 (2024). https://doi.org/10.1007/s11042-023-17487-z

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