Abstract
In recent years, a series of research experiments have been conducted on WiFi-based gesture recognition. However, current recognition systems are still facing the challenge of small samples and environmental dependence. To deal with the problem of performance degradation caused by these factors, we propose a WiFi-based gesture recognition system, WiGAN, which uses Generative Adversarial Network (GAN) to extract and generate gesture features. With GAN, WiGAN expands the data capacity to reduce time cost and increase sample diversity. The proposed system extracts and fuses multiple convolutional layer feature maps as gesture features before gesture recognition. After fusing features, Support Vector Machine (SVM) is exploited for human activity classification because of its accuracy and convenience. The key insight of WiGAN is to generate samples and merge multi-grained feature maps in our designed GAN, which not only enhances the data but also allows the neural network to select different grained features for gesture recognition. According to the result of experiments conducted on two existing datasets, the average recognition accuracy of WiGAN reaches 98% and 95.6%, respectively, outperforming the existing system. Moreover, the recognition accuracy under different experimental environments and different users shows the robustness of WiGAN.
Highlights
With the rapid development of virtual reality and smart home, human–computer interaction applications are becoming more and more popular in our life
We propose a WiFi-based gesture recognition system, WiGAN, which addresses the problem of performance degradation caused by small samples and the environmental dependence
For WiGAN, combining the structure of Deep Convolution Generation Adversarial Network (DCGAN) [35] and the characteristics of Conditional Generative Adversarial Network (CGAN) [36], we propose a conditional convolution generation adversarial network, which can control the generation of small samples using control conditions
Summary
With the rapid development of virtual reality and smart home, human–computer interaction applications are becoming more and more popular in our life. The WiKey [12] proposed by Ali et al is a classic human–computer interaction system, which can perform key recognition based on the CSI changes caused by the subtle movements of human fingers during typing It is non-uniform for the usage rate of each key, which results in a class imbalance problem in offline training. If the fall data are forcibly collected, there will be a high time cost These small sample problems have affected the popularity and application of gesture recognition algorithms. We propose a WiFi-based gesture recognition system, WiGAN, which addresses the problem of performance degradation caused by small samples and the environmental dependence.
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