Convolutional neural network-based human body behavior recognition method and recognition system

A technology of convolutional neural network and recognition method, which is applied in the field of human daily behavior, and can solve the problems of large individual differences among users, low recognition accuracy of human behavior, and not all behavior determination methods are applicable and effective.

Inactive Publication Date: 2018-07-31
EAST CHINA NORMAL UNIV
View PDF6 Cites 28 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] One of the common smartphone-based human behavior recognition methods is implemented by using the Google Activity Recognition API (Google Activity Recognition API), which can recognize human behaviors such as cycling, running, walking, and stillness. , but the accuracy rate of human behavior recognition realized through this interface is very low, the reason is that the user's motion patterns (movement speed, gait, etc.) For people of gender, the relatively single and fixed methods of judging behavioral activities are not all applicable and effective

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Convolutional neural network-based human body behavior recognition method and recognition system
  • Convolutional neural network-based human body behavior recognition method and recognition system
  • Convolutional neural network-based human body behavior recognition method and recognition system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be further described in detail in conjunction with the following specific embodiments and accompanying drawings. The process, conditions, experimental methods, etc. for implementing the present invention, except for the content specifically mentioned below, are common knowledge and common knowledge in this field, and the present invention has no special limitation content.

[0037]The core content of the present invention is to establish a convolutional neural network model that can effectively identify the daily behavior of the human body on the basis of simple data preprocessing. The data processed by the model is the original three-axis acceleration collected by the three-axis acceleration sensor in the smart phone. Value and user feedback behavior labels, the predicted behaviors include walking, jogging, going up and down stairs, standing and sitting and other six daily behaviors. After multiple training, optimization and testing of the mod...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention proposes a convolutional neural network-based human body behavior recognition method. The method comprises three stages, namely, a data acquisition and preprocessing stage, a model establishment and training stage and a model application stage. According to the method of the invention, an established convolutional neural network structure can effectively identify six kinds of daily human behaviors on the basis of simple data preprocessing and provide a good foundation for transplanting the recognition method into a smart phone with limited computing power. The method can be applied to application fields such as smart phone end-based human body daily activity recording, health tracking and health monitoring.

Description

technical field [0001] The invention relates to six kinds of human daily behaviors, including walking, jogging, going up and down stairs, standing and sitting, state analysis and health monitoring, etc., specifically a human behavior recognition method and recognition system based on a convolutional neural network. Background technique [0002] With the development of information technology, smart phones integrating various sensors (such as three-axis acceleration sensors and gyroscopes, etc.) are becoming more and more popular. In recent years, more and more application software based on human behavior recognition based on mobile phone sensors have been developed to record and monitor the daily behavior of the human body, and at the same time can effectively help users establish a healthy living habit. In this field, seeking An efficient identification method of human physiological activities has become the most critical, core and urgent problem. [0003] One of the common...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/20G06N3/045
Inventor 徐文超庞雨欣杨艳琴陈晓琛宋凡迪黄雪峰
Owner EAST CHINA NORMAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap