Human activity gesture recognition method based on multi-level end-to-end neural network

Abstract

The invention discloses a human activity gesture recognition method based on a multi-level end-to-end neural network, comprising the following steps: collecting labeled data of human activity gestures, cutting the labeled data by using a sliding window, and obtaining several segments of equidistant A first labeled data window, processing the first labeled data window using a gait-based data augmentation algorithm to obtain segments of a second labeled data window; using the first labeled data window and the second labeled data window Windowed pair multi-layer end-to-end neural network training. Collect the raw data of any human activity posture, and perform sliding window cutting on the raw data to obtain several consecutive motion data windows to be recognized; import the motion data windows to be recognized into the trained multi-level terminal in sequence Peer-to-peer neural network to identify the type of human activity gestures. The invention has the advantages of high recognition accuracy, low calculation complexity, low power consumption, etc., and has broad market prospects in the fields of medical technology, behavior supervision and the like.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a human activity posture recognition method based on a multi-level end-to-end neural network. Background technique [0002] Human behavior recognition has always been a very popular research field. Its purpose is to analyze and identify human action types and behavior patterns through a series of observations, and describe them using natural language. With the breakthrough of machine learning algorithms, the accuracy of human behavior recognition is getting higher and higher, which makes human behavior recognition enter all aspects of life. Human behavior recognition technology has broad application prospects and considerable economic value, and the application fields involved mainly include: video surveillance, medical diagnosis and monitoring, intelligent human-computer interaction, virtual reality, etc. In the field of video surveillance, traditional video surveillance mainl...

AI Technical Summary

Problems solved by technology

[0005] First, it is usually necessary to extract a large number of complex features, which introduces a large amount of power consumption

[0006] Second, when there are new behaviors, it may take a lot of effort to find new features

[0007] Third, due to the limitations of human knowledge and experience, the recognition accuracy is often limited

However, there are situations in which some behavioral characteristics are similar in existing structures, making it difficult to distinguish human activities and behaviors

Not only that, due to the neural network involved, the computational complexity is relatively high, and the power consumption is relatively high, and the neural network also requires a large amount of training data

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