Wireless cross-domain action recognition method based on semi-supervised learning

The invention provides a wireless cross-domain action recognition method based on semi-supervised learning, and the method comprises a step of extracting the amplitude information of each subcarrier from the CSI data of a WiFi signal for action recognition according to the principle that a human body action affects a wireless signal; in order to solve the problem that a large amount of annotationdata is not easy to obtain, the method only annotates a real action label for a small amount of action samples, then calculates the similarity between a label-free sample and a labeled sample througha DTW algorithm, and attaches a pseudo label to the label-free sample, thereby expanding a training sample set. In order to improve the generalization of an action recognition model and realize actionrecognition of different positions or different persons, the invention provides a classification clustering comprehensive model, establishes an SOM network to cluster action samples, and finally classifies the samples in combi