A gesture recognition method based on wifi signal

Abstract

The invention discloses a gesture recognition method based on WiFi signals, the steps are: S1, using a notebook computer and a router installed with an Intel5300 wireless network card to collect CSI data of 0-9 digital gestures; S2, extracting Ntx from the collected gesture data The 3-dimensional CSI matrix of *Nrx*30; S3, perform data preprocessing on the extracted CSI matrix data; S4, perform feature extraction and reconstruction on the preprocessed CSI matrix, and construct it into a convolutional autoencoder that can be processed matrix; S5, building a convolutional autoencoder model, and performing feature extraction and classification on the CSI matrix obtained in S4. In this method, the user does not need to wear or rely on any sensors, but only needs to use the ubiquitous WiFi, process the channel state information in WiFi, and then use the convolutional autoencoder to extract and classify its features, so as to realize the recognition of 10 gestures. identification.

Description

technical field [0001] The invention relates to the technical field of wireless communication and artificial intelligence recognition, in particular to a gesture recognition method based on WiFi signals. Background technique [0002] With the development of wireless communication and artificial intelligence technology, gesture recognition is becoming an important pillar of smart home automation and human-computer interaction, and gesture recognition has great potential for many other emerging applications in home automation, such as adjusting temperature and brightness levels , for personalised thermal comfort and remote control of household equipment. Some studies have proposed to use wearable devices to recognize human activities. However, because wearable devices are expensive and inconvenient to carry, this method has not been widely promoted. Some studies have proposed the use of RGB cameras and infrared depth localization cameras for gesture recognition based on visio...

AI Technical Summary

Problems solved by technology

Some studies have proposed using wearable devices to identify human body activities, but this method has not been widely promoted because wearable devices are expensive and inconvenient to carry.

Some studies have proposed the use of RGB cameras and infrared depth positioning cameras for gesture recognition based on vision systems. Although their recognition accuracy is satisfactory, the need for favorable lighting conditions and potential privacy issues hinder their popular implementation.

There is also the use of WiFi channel state information CSI to realize gesture recognition, but most of them use traditional machine learning to build recognition systems. The data denoising and training process is cumbersome and requires professional knowledge. The gesture classifier uses KNN and other algorithms. To achieve a certain recognition effect, but the traditional machine learning technology needs human operation when extracting representative features related to various gestures, which will lead to a decrease in recognition accuracy

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