A wireless cross-domain action recognition method based on semi-supervised learning

Abstract

The present invention proposes a wireless cross-domain action recognition method based on semi-supervised learning. According to the principle that human actions affect wireless signals, the amplitude information of each subcarrier is extracted from the CSI data of the WiFi signal for action recognition. In order to solve the problem that it is difficult to obtain a large amount of labeled data, the present invention only labels a small number of action samples with real action labels, and then calculates the similarity between unlabeled samples and labeled samples through the DTW algorithm, and pastes pseudo-labels on unlabeled samples, thereby Expand the training sample set. In order to increase the generalization of the action recognition model and realize the action recognition of different positions or different people, the present invention proposes a classification-clustering comprehensive model, establishes a SOM network to cluster action samples, and combines the results of classification and clustering to perform final analysis on the samples. Classification. The invention reduces the dependence of the supervised learning model on a large number of labeled samples, uses only a small number of labeled samples combined with unlabeled samples to train the classification model, has high generalization, and can realize cross-domain action recognition.

Description

technical field [0001] The invention relates to an action recognition method based on wireless signals, in particular to a cross-domain action recognition method based on commercial WiFi using meta-learning and semi-supervised learning. Background technique [0002] Action recognition is of great significance in people's daily life. With the rapid development of the Internet of Things, pervasive computing, graphics and images, artificial intelligence and other fields, it has become possible to accurately identify human actions through technical means and has gradually been widely used. Monitoring people's daily behaviors through motion recognition can effectively ensure people's health and safety in their daily lives. Currently commonly used motion recognition technologies are mainly based on video surveillance or wearable devices with built-in sensors. The video surveillance method needs to work normally under the conditions of light and no occlusion, and it is difficult ...

AI Technical Summary

Problems solved by technology

This is a supervised learning method, and its disadvantages are: (1) a large amount of labeled sample data is required to train the recognition model, and the collection and labeling of labeled sample data are expensive; (2) the classification of a single classifier Poor performance and generalization performance, when recognizing actions from different domains (such as different locations or different people), the classification ability is greatly reduced

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