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Human body behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning

A time-frequency feature and recognition method technology, applied in the field of human behavior recognition, to achieve the effect of improving recognition accuracy

Active Publication Date: 2021-06-01
FUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional human behavior recognition methods based on WiFi signals, especially those using coarse-grained signals, are often difficult to achieve high-precision recognition for some behaviors with similar patterns.

Method used

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  • Human body behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning
  • Human body behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning
  • Human body behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning

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Embodiment Construction

[0077] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0078] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0079] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention relates to a human body behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning, and the method comprises the following steps: S1, building a passive human body behavior recognition system based on coarse-grained signals; s2, collecting coarse-grained signal data under different behaviors of a human body; s3, performing sample set construction and signal preprocessing; s4, constructing a human body behavior shallow layer feature vector based on the coarse-grained signal; s5, constructing a deep learning model; s6, constructing an integrated learning model; and step S7, utilizing the sensing signal sequence to achieve behavior identification; The technical means of passive human body behavior recognition can be enriched, and the recognition accuracy of similar behaviors based on coarse-grained signals is improved.

Description

technical field [0001] The invention relates to the technical field of human behavior recognition, in particular to a human behavior recognition method based on coarse-grained time-frequency features and multi-level fusion learning. Background technique [0002] In recent years, the rapid development of Internet of Things technology and artificial intelligence has promoted the mutual "communication" between people and things, and things and things, and greatly improved the way of life of human beings. Human Behavior Recognition (HBR) technology, as one of the research hotspots in the intelligentization of the Internet of Things, can provide technical support for the realization of intelligent and humanized human-computer interaction services. It has broad application prospects. For example, in terms of smart health care, people sitting quietly in front of the computer for a long time will make people in a sub-healthy state. Through the identification of this behavior, an ea...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/40G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/103G06V10/30G06V10/44G06N3/048G06N3/045G06F18/2411G06F18/2415G06F18/214Y02D30/70
Inventor 陈静黄新宇江灏缪希仁
Owner FUZHOU UNIV