A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data

Physical data is an important aspect of urban data, which provides a guarantee for the healthy development of smart cities. Students’ physical health evaluation is an important part of school physical education, and postural recognition plays a significant role in physical sports. Traditional postur...

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Main Authors: Dianchen He, Li Li
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2020/8817419
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spelling doaj-b405ef3e28f947d488f8db4afbe149472020-11-25T02:05:58ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772020-01-01202010.1155/2020/88174198817419A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban DataDianchen He0Li Li1College of Sports Science, Woosuk University, Jeonju, Republic of KoreaCollege of Sports Science, Woosuk University, Jeonju, Republic of KoreaPhysical data is an important aspect of urban data, which provides a guarantee for the healthy development of smart cities. Students’ physical health evaluation is an important part of school physical education, and postural recognition plays a significant role in physical sports. Traditional posture recognition methods are with low accuracy and high error rate due to the influence of environmental factors. Therefore, we propose a new Kinect-based posture recognition method in a physical sports training system based on urban data. First, Kinect is used to obtain the spatial coordinates of human body joints. Then, the angle is calculated by the two-point method and the body posture library is defined. Finally, angle matching with posture library is used to analyze posture recognition. We adopt this method to automatically test the effect of physical sports training, and it can be applied to the pull-up of students’ sports. The position of the crossbar is determined according to the depth sensor information, and the position of the mandible is determined by using bone tracking. The bending degree of the arm is determined through the three key joints of the arm. The distance from the jaw to the bar and the length of the arm are used to score and count the movements. Meanwhile, the user can adjust his position by playing back the action video and scoring, so as to achieve a better training effect.http://dx.doi.org/10.1155/2020/8817419
collection DOAJ
language English
format Article
sources DOAJ
author Dianchen He
Li Li
spellingShingle Dianchen He
Li Li
A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
Wireless Communications and Mobile Computing
author_facet Dianchen He
Li Li
author_sort Dianchen He
title A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
title_short A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
title_full A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
title_fullStr A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
title_full_unstemmed A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data
title_sort new kinect-based posture recognition method in physical sports training based on urban data
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2020-01-01
description Physical data is an important aspect of urban data, which provides a guarantee for the healthy development of smart cities. Students’ physical health evaluation is an important part of school physical education, and postural recognition plays a significant role in physical sports. Traditional posture recognition methods are with low accuracy and high error rate due to the influence of environmental factors. Therefore, we propose a new Kinect-based posture recognition method in a physical sports training system based on urban data. First, Kinect is used to obtain the spatial coordinates of human body joints. Then, the angle is calculated by the two-point method and the body posture library is defined. Finally, angle matching with posture library is used to analyze posture recognition. We adopt this method to automatically test the effect of physical sports training, and it can be applied to the pull-up of students’ sports. The position of the crossbar is determined according to the depth sensor information, and the position of the mandible is determined by using bone tracking. The bending degree of the arm is determined through the three key joints of the arm. The distance from the jaw to the bar and the length of the arm are used to score and count the movements. Meanwhile, the user can adjust his position by playing back the action video and scoring, so as to achieve a better training effect.
url http://dx.doi.org/10.1155/2020/8817419
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