Human body activity recognition method based on grouping residual joint spatial learning

Abstract

A human body activity recognition method based on grouping residual joint spatial learning comprises the following steps: step 1, collecting human, object and environment signals by using various sensors, grouping, aligning and slicing single-channel data based on a sliding window, and constructing a two-dimensional activity data subset; step 2, building a grouping residual convolutional neural network, and constructing a joint space loss function optimization network model by utilizing a center loss function and a cross entropy loss function in order to extract a feature map of a two-dimensional activity data subset; and step 3, training a multi-classification support vector machine by utilizing the extracted two-dimensional features to realize a human body activity classification task based on the feature map. According to the invention, fine human body activities can be identified; the inter-class distance of the extracted spatial features is increased in combination with a joint spatial loss function, and the intra-class distance is reduced; based on the spatial feature map of the human body activity data, a multi-classification support vector machine is combined to carry outclassification learning on the feature map, and the accuracy of human body activity classification is improved.

Description

technical field [0001] The present invention relates to the problem of human body activity recognition, in particular to an activity recognition method based on human-computer interaction and deep learning. The invention realizes human body activity recognition based on group residual joint convolutional neural network and support vector machine, and belongs to the field of combining human-computer interaction, activity recognition and deep learning. Background technique [0002] With the advent of deep learning, the concept of human-computer interaction has been further developed in the context of current applications. The future world will be a world where everything is interconnected, where people and the environment are gradually integrated, and information on human activities needs to be collected in real time and processed effectively. Therefore, the research on human activity recognition has received extensive attention from researchers. The research on human activi...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the low accuracy of existing human activity recognition methods and to solve the problem of human activity recognition based on sensor data, the present invention proposes a human activity recognition method based on group residual joint space learning, which effectively solves the problem of Solved the problem of human activity recognition and improved the accuracy of human activity recognition

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