Few-shot Learning for Human Activity Recognition Based on CSI

Human Activity Recognition(HAR) based on Channel State Information(Csi)plays an increasingly impor-tant role in human-computer interaction. Traditional research requires a large amount of activity sample to train network model. However, collecting a great many of data causes waste of time and manpower. Some models can well identify the categories of activities in this scene, but when another scene is tested, the identification accuracy of the model will be reduced. Therefore it needs to re-collect data to retrain the model. We proposed a method which can transfer the knowledge learned from a scenario to a new scenario. It can also facilitate the model's knowledge learning from the source domain and quickly generalize to new tasks that contain only a small number of samples. Through this method, the model that maintains high accuracy and scalability to identify new category, we added attention mechanism can automatically extract features that are useful to the model and ignore some noise that negatively affects the model, meanwhile improve system stability and the effectiveness of activity recognition. We also performed the scaling and shifting(SS) transformation on the network, which could reduce the parameters of the model, improve the training speed, and avoid overfitting.