Intelligent Recognition of Students' Incorrect Movements in Physical Education using Virtual Reality-based Computer Pattern Recognition

被引：0

作者：

Wang L. ^{[1
]}

Xu S. ^{[1
]}

机构：

[1] Physical Education Department of North China Electric Power University, Beijing

来源：

Computer-Aided Design and Applications | 2023年 / 20卷 / S14期

关键词：

computer pattern recognition; intelligent recognition; students; virtual reality; wrong actions;

D O I：

10.14733/cadaps.2023.S14.192-207

中图分类号：

学科分类号：

摘要：

The use of convolutional neural network methods for computer vision research requires a large amount of labeled data. With the emergence of labeled data sets in different fields and the successive release of deep learning open source frameworks, the development of deep learning has been further promoted.This paper combines computer pattern recognition algorithms to intelligently recognize the wrong actions of students, and improve the standard of students' actions. Moreover, starting from the inherent characteristics of the human body, this paper designs a brand multi-person analysis network based on human body posture region extraction and posture correction.In addition, this paper constructs an intelligent recognition system for students' wrong actions based on computer pattern recognition. Through experimental research, it can be known that the intelligent recognition system of students' wrong actions based on computer pattern recognition proposed in this paper has good results. © 2023, CAD Solutions, LLC. All rights reserved.

引用

页码：192 / 207

页数：15

共 23 条

[1]

Aso K., Hwang D. H., Koike H., Portable 3D Human Pose Estimation for Human-Human Interaction using a Chest-Mounted Fisheye Camera, Augmented Humans Conference 2021, 2021, pp. 116-120

[2]

Azhand A., Rabe S., Muller S., Sattler I., Heimann-Steinert A., Algorithm based on one monocular video delivers highly valid and reliable gait parameters, Scientific Reports, 11, 1, pp. 1-10, (2021)

[3]

Bakshi A., Sheikh D., Ansari Y., Sharma C., Naik H., Pose Estimate Based Yoga Instructor, International Journal of Recent Advances in Multidisciplinary Topics, 2, 2, pp. 70-73, (2021)

[4]

Colyer S. L., Evans M., Cosker D. P., Salo A. I., A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system, Sports Medicine-open, 4, 1, pp. 1-15, (2018)

[5]

Dang Q., Yin J., Wang B., Zheng W., Deep learning based 2d human pose estimation: A survey, Tsinghua Science and Technology, 24, 6, pp. 663-676, (2019)

[6]

Diaz R. G., Laamarti F., El Saddik A., DTCoach: Your Digital Twin Coach on the Edge During COVID-19 and Beyond, IEEE Instrumentation & Measurement Magazine, 24, 6, pp. 22-28, (2021)

[7]

Ershadi-Nasab S., Noury E., Kasaei S., Sanaei E., Multiple human 3d pose estimation from multiview images, Multimedia Tools and Applications, 77, 12, pp. 15573-15601, (2018)

[8]

Gu R., Wang G., Jiang Z., Hwang J. N., Multi-person hierarchical 3d pose estimation in natural videos, IEEE Transactions on Circuits and Systems for Video Technology, 30, 11, pp. 4245-4257, (2019)

[9]

Hua G., Li L., Liu S., Multipath affinage stacked—hourglass networks for human pose estimation, Frontiers of Computer Science, 14, 4, pp. 1-12, (2020)

[10]

Li M., Zhou Z., Liu X., Multi-person pose estimation using bounding box constraint and LSTM, IEEE Transactions on Multimedia, 21, 10, pp. 2653-2663, (2019)

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