The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model

Perceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the saf...

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Main Author: Chaojie Xi
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Function Spaces
Online Access:http://dx.doi.org/10.1155/2022/8339745
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author Chaojie Xi
author_facet Chaojie Xi
author_sort Chaojie Xi
collection DOAJ
description Perceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the safety of athletes in bodybuilding training, this paper uses Kinect to hold the coach’s body contour, determine the standard level of coaches’ sports, and combine the characteristics for aerobics training, so as to improve the sports level of coaches, through data acquisition, data processing, and feature extraction to assist sports learning, as well as human posture recognition. The calculation and recognition of human skeleton joints are completed by two algorithms, which improve the human motion recognition algorithm. The aerobics data collected by Kinect device is specified and digitized, which enhances the robustness of the system and improves the performance of the algorithm and the accuracy of the motion data.
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publisher Wiley
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series Journal of Function Spaces
spelling doaj-art-11cba0fd855e42a4b89984c649a13a1c2025-02-03T05:57:23ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/8339745The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition ModelChaojie Xi0Department of SportsPerceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the safety of athletes in bodybuilding training, this paper uses Kinect to hold the coach’s body contour, determine the standard level of coaches’ sports, and combine the characteristics for aerobics training, so as to improve the sports level of coaches, through data acquisition, data processing, and feature extraction to assist sports learning, as well as human posture recognition. The calculation and recognition of human skeleton joints are completed by two algorithms, which improve the human motion recognition algorithm. The aerobics data collected by Kinect device is specified and digitized, which enhances the robustness of the system and improves the performance of the algorithm and the accuracy of the motion data.http://dx.doi.org/10.1155/2022/8339745
spellingShingle Chaojie Xi
The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
Journal of Function Spaces
title The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
title_full The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
title_fullStr The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
title_full_unstemmed The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
title_short The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model
title_sort construction of adaptive learning for sports based on aerobics trajectory recognition model
url http://dx.doi.org/10.1155/2022/8339745
work_keys_str_mv AT chaojiexi theconstructionofadaptivelearningforsportsbasedonaerobicstrajectoryrecognitionmodel
AT chaojiexi constructionofadaptivelearningforsportsbasedonaerobicstrajectoryrecognitionmodel