A method and a device for human body flexion angle recognition based on BP neural network

A BP neural network and angle technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as affecting the efficiency of detection, increasing measurement errors, etc., to improve measurement efficiency, overcome measurement errors, and measure accurate effect

Inactive Publication Date: 2019-01-15
GUANGDONG POLYTECHNIC NORMAL UNIV
View PDF6 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practice, it is found that this measurement method has serious defects, because different people will have an impact on the distance of the fingers due to different limb lengths, which will increase the measurement error and affect the efficiency of detection.

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
  • A method and a device for human body flexion angle recognition based on BP neural network
  • A method and a device for human body flexion angle recognition based on BP neural network
  • A method and a device for human body flexion angle recognition based on BP neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] The present embodiment provides a kind of human body flexion angle recognition method based on BP neural network, such as image 3 as shown, image 3 It is a flow chart of a human body flexion angle recognition method based on a BP neural network according to Embodiment 1 of the present invention, comprising the following steps:

[0045] Step S301: Obtain side images of sitting and forward bending of multiple testers;

[0046] In the embodiment of the present invention, a large number of sample images are obtained by shooting side images of multiple testers when they are doing sitting body forward bending. The side background of the sitting and forward bending project is the same, so as to ensure the unity of multiple sample images;

[0047] Step S302: Obtain the body flexion angles of multiple testers by performing feature marks on the seated and forward-bending side images of multiple testers;

[0048] In the embodiment of the present invention, the specific implem...

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 discloses a human body flexion angle identification method and a device based on a BP neural network, wherein, the method comprises the following steps of: establishing a BP neural network model; the foreground object of the sitting body flexion side image is extracted, and the sitting body flexion side image after the foreground object is extracted is obtained. Inputting the sittingbody flexion side image into the BP neural network model for body flexion angle identification; outputting the body flexion angle of the subject. The invention can overcome the measurement error caused by different limb lengths, makes the measurement result more accurate, and further improves the detection efficiency.

Description

technical field [0001] The invention relates to the field of human body flexibility detection, in particular to a BP neural network-based method and equipment for identifying the bending angle of a human body. Background technique [0002] At present, in my country, the flexibility of the human body is usually tested by using the methods of sitting forward bending and standing forward bending. The evaluation index is the maximum distance that can be reached by finger extension, and the distance of finger or head extension when flexing is used instead of joints. The angle of activity is measured. However, in practice, it is found that this measurement method has serious defects, because different people will have an impact on the distance of the fingers due to different limb lengths, which increases the measurement error and affects the detection efficiency. Contents of the invention [0003] The invention provides a BP neural network-based recognition method and equipment ...

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): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/23G06N3/045G06F18/214
Inventor 卢旭杨川
Owner GUANGDONG POLYTECHNIC 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