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TARF: Technology-Agnostic RF Sensing for Human Activity Recognition.

Chao Yang, Xuyu Wang, Shiwen Mao

    IEEE Journal of Biomedical and Health Informatics
    |May 20, 2022
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    Summary

    This study introduces TARF, a technology-agnostic approach for human activity recognition (HAR) using radio-frequency (RF) sensing. TARF effectively integrates data from diverse RF technologies, enhancing HAR system performance and reducing deployment barriers.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Signal Processing

    Background:

    • Smart Internet of Things (IoT) necessitates advanced human activity recognition (HAR).
    • Existing radio-frequency (RF) sensing technologies (e.g., WiFi, RFID, FMCW radar) offer non-invasive HAR capabilities.
    • A unified HAR solution adaptable to multiple RF technologies is crucial for cost-effective and robust deployment.

    Purpose of the Study:

    • To propose a technology-agnostic approach for RF-based HAR, named TARF.
    • To enable seamless integration of data from diverse RF sensing devices.
    • To enhance the robustness and reduce the deployment cost of HAR systems.

    Main Methods:

    • Developed a novel data generalization technique to address data disparities across different RF devices.
    • Implemented a domain adversarial neural network to manage interference from various RF sensing technologies.
    • Evaluated the TARF system using four distinct RF sensing technologies.

    Main Results:

    • The proposed TARF system demonstrated effective performance across multiple RF sensing technologies.
    • The data generalization technique successfully mitigated data heterogeneity.
    • The domain adversarial network effectively handled inter-technology interference.

    Conclusions:

    • TARF offers a versatile and effective solution for RF-based human activity recognition.
    • The technology-agnostic approach significantly outperforms state-of-the-art Convolutional Neural Network (CNN)-based methods.
    • TARF paves the way for more integrated and robust HAR systems in IoT applications.