A Gesture Recognition Method Based on Feature Fusion of Heterogeneous Sensors

Abstract

The invention discloses a gesture recognition method for feature fusion of heterogeneous sensors. Firstly, the original signal is obtained by using an EMG sensor, a visual sensor and an inertial sensor, and then data preprocessing is performed; further, the processed data is divided into a training set and a training set. The test set; secondly, input the training set into the built convolutional network model for training, and introduce an attention mechanism for different sensor information during training; further, input the test set into the trained convolutional network model to extract heterogeneous The sensor feature vector; finally, construct the Copula connection function to construct the feature fusion model, and use the support vector machine to classify. The present invention uses heterogeneous sensors to obtain data, combines convolutional neural network to extract features, and uses Copula to fuse different sensor features, which better expresses the correlation between sensor features and improves the accuracy of gesture recognition. Provide technical support for the development of human-computer interaction.

Description

technical field [0001] The invention relates to the technical field of human-computer interaction, in particular to a gesture recognition method for heterogeneous sensor feature fusion. Background technique [0002] Gesture recognition is widely used in intelligent transportation, intelligent factories, intelligent robots and other fields. Heterogeneous sensors provide rich multimodal information for gesture recognition in order to achieve more intelligent and convenient functions. However, in order to improve the accuracy and robustness of gesture recognition and classification, how to extract deep features from heterogeneous sensors and effectively fuse them remains to be studied. [0003] With the improvement of artificial intelligence technology and computer performance, convolutional neural networks are widely used in various fields. Multi-scale deep semantic information can be extracted through convolutional neural networks, and gesture recognition and classification ...

AI Technical Summary

Problems solved by technology

[0005] In view of the above problems, the purpose of the present invention is to provide a gesture recognition method for feature fusion of heterogeneous sensors, extract rich multi-scale feature information through convolutional neural network, and use Copula function to effectively fuse the feature vectors of heterogeneous sensors. It increases the accuracy of gesture recognition to a certain extent, solves the problem that single-sensor features are susceptible to interference, and provides technical support for the subsequent development of human-computer interaction

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