Robust CSI-Based Human Activity Recognition With Augment Few Shot Learning

With the development of Internet of Things technology, Channel State Information (CSI) based human activity recognition (HAR) plays an important role in Human-Computer Interaction and achieves considerable advancements over recent years. However, when the trained model is applied to recognize new activity categories or recognizing new users in new scenarios, the recognition performance of general methods will dramatically decline. And re-collecting adequate new activity categories' samples to train the HAR model to adapt to the new situation will consume a lot of time and human effort. To overcome this challenge, we propose a framework, Augment Few Shot Learning-based Human Activity Recognition (AFSL-HAR), which can achieve significant performance in recognizing new categories through a small amount of samples to fine-tune the model parameters and avoid retraining the network from scratch again. And besides, in order to improve the robustness of AFSL-HAR, we design a Feature Wasserstein Generative Adversarial Network (FWGAN) module, which can synthesize diverse samples to help the recognition model learn more sharper classification boundaries. Specifically, the FWGAN module incorporates a feature extractor to realize converging with a fewer number of training samples, and takes an improved discriminator to enhance system performance. The experimental results demonstrate that AFSL-HAR can achieve accuracy of 98.9% and 94.7% when recognizing new activities using few samples for each category on the public data set and self-made data set, respectively.