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WiGAN: A WiFi Based Gesture Recognition System with GANs.

Dehao Jiang^1,2,3, Mingqi Li¹, Chunling Xu¹

¹Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

Sensors (Basel, Switzerland)

|August 27, 2020

Summary

This summary is machine-generated.

WiGAN, a WiFi-based gesture recognition system, uses Generative Adversarial Networks (GAN) to improve accuracy by expanding datasets and generating diverse gesture features. This robust system achieves high recognition rates across various environments and users.

Keywords:

channel status information generate adversarial network gesture recognition support vector machine wireless

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Area of Science:

Computer Science
Artificial Intelligence
Signal Processing

Background:

WiFi-based gesture recognition systems face challenges with limited data and environmental variability.
Performance degradation in current systems necessitates novel approaches for robust feature extraction and data augmentation.

Purpose of the Study:

To propose WiGAN, a novel WiFi-based gesture recognition system utilizing Generative Adversarial Networks (GAN).
To address limitations of small sample sizes and environmental dependence in existing gesture recognition technologies.

Main Methods:

Employing Generative Adversarial Networks (GAN) for gesture feature extraction and generation, expanding data capacity and diversity.
Implementing a fusion strategy for multi-convolutional layer feature maps as gesture features.
Utilizing Support Vector Machine (SVM) for accurate human activity classification.

Main Results:

WiGAN achieved average recognition accuracies of 98% and 95.6% on two benchmark datasets.
Demonstrated superior performance compared to existing WiFi-based gesture recognition systems.
Exhibited high recognition accuracy across different experimental environments and users, indicating robustness.

Conclusions:

WiGAN effectively enhances gesture recognition by leveraging GAN for data augmentation and feature fusion.
The proposed system offers a robust and accurate solution for WiFi-based gesture recognition, overcoming common limitations.
The integration of GAN and SVM provides a powerful framework for advanced human activity recognition.