research-article

Research on sports injury recovery detection based on infrared thermography

Authors:

Yu DuanAuthors Info & Claims

ICDLT '23: Proceedings of the 2023 7th International Conference on Deep Learning Technologies

Pages 15 - 20

https://doi.org/10.1145/3613330.3613344

Published: 28 September 2023 Publication History

Abstract

In this paper, with the research objective of detecting the degree of sports injury recovery with high accuracy and efficiency, an infrared thermography-based recovery detection method for sports injury is proposed. The temperature at the sports injury is detected by the infrared thermography-based temperature detection method, and the results are presented as infrared thermal images of the human body based on temperature differences. For the acquired infrared thermal images of the human body based on temperature difference, a support vector machine classification algorithm-based recovery degree evaluation method of sports injury is used to calculate the optimal classification function of infrared thermal images to classify the sports injury recovery degree and complete the sports injury recovery detection. The experimental results prove that the proposed method is consistent with the actual situation, which verifies the effectiveness of the method for sports injury recovery detection; the method has better image quality with larger infrared thermal imaging standard deviation and gradient average in detecting sports injury recovery degree, and the method can shorten the recovery detection time of sports injury and improve the detection efficiency.

References

[1]

Xu Y, Wang Q, Luo C, Zheng Q. 2020. Nondestructive debonding detection of fiber reinforced plastics strengthened concrete structure based on infrared thermal imaging with laser thermal excitation. Acta Materiae Compositae Sinica. 37, 2 (February. 2020), 472-481. https://dx.doi.org/10.13801/j.cnki.fhclxb.20190428.001.

[2]

Kafieh, R.Lotfi, T.Amirfattahi, R. 2011. Automatic detection of defects on polyethylene pipe welding using thermal infrared imaging. Infrared Physics & Technology, 54, 4(July. 2011), 317-325. https://doi.org/10.1016/j.infrared.2010.12.010.

[3]

Wei H, Wu L, Zhu Y. 2019. The excitation source for the infrared thermography based on FPGA. Laser & Infrared. 49, 6(June. 2019), 731-736. https://dx.doi.org/10.3969/j.issn.1001-5078.2019.06.017.

[4]

Liu Y, Tang J, Guo X, Yang D, Yue X. 2017. Effects of composites translucence on flash infrared thermal detection. Laser & Infrared. 47, 10 (October. 2017), 1264-1270. https://dx.doi.org/10.3969/j.issn.1001-5078.2017.10.014.

[5]

Zhao S, Guo X, Liu Y. 2018. Infrared modulated thermography based on complex Morlet wavelet phase. Infrared and Laser Engineering. 47, 9 (September. 2018), 55–62. https://dx.doi.org/10.3788/IRLA201847.0904002.

[6]

Zhu H, Liu C, Zhang B, & Cai G. 2018. Research on Laser Ultrasonic Visual Image Processing. Chinese Journal of Lasers. 45, 1 (January. 2018), 168-175. https://dx.doi.org/10.3788/CJL201845.0104004.

[7]

Yang A, Liu D, Gao Y. 2017. Propagation and Prejudgement Technique of Hot Images in High Power Laser System. Chinese Journal of Lasers. 44, 9 (September. 2017), 42-49. https://dx.doi.org/10.3788/CJL201744.0901006.

[8]

Feng H, Shi Y, Li Q. 2017. Design of The Motion Detection System Based on Pyroelectric Infrared Sensor. Piezoelectrics & Acoustooptics. 39, 4 (August. 2017), 610-613. https://dx.doi.org/10.3778/j.issn.1002-8331.1508-0022.

[9]

Jin X Y, Chen J L. 2017. Research on infrared thermography non-destructive testing technologyby square pulse excitation. Laser & Infrared, 47, 7 (July. 2017), 823-827. https://doi.org/10.3969/j.issn.1001-5078.2017.07.007.

[10]

Li X, Shi Y, Song Y, 2017. Study on Near-infrared Imaging System Based on AOTF. Piezoelectrics & Acoustooptics, 39, 2(April. 2017), 289-294. https://doi.org/10.11896/j.issn.1002-137X.2017.03.057.

[11]

Wang B Z, Dong L H, Wang H D, 2020. Research and Application of Laser Infrared Thermography in Material Defect Detection. Materials Reports, 34, 5 (May. 2020), 127-132. https://doi.org/10.11896/cldb.18110095.

[12]

Gu J W, Xie X P, Cao Y B, 2020. A Semi-Dense Depth Map Acquisition Algorithm Based on Laser Speckle. Chinese Journal of Lasers, 47, 3(January. 2020), 199-207. https://dx.doi.org/10.3788/CJL202047.0304004.

[13]

Li G H, Lei J. 2017. Research on Major Technology of PCB Infrared Thermal Detection Based on Information Fusio. Infrared Technology. 39, 9 (October. 2017), 829-834. https://dx.doi.org/10.3969/j.issn.1001-3881.2018.06.025.

[14]

Qin Y, Wu J, An W. 2017. Laser marking grid image feature extraction based on RANSAC. Computer Engineering and Science.39, 8 (September. 2017), 1495-1501. https://dx.doi.org/10.3969/j.issn.1007-130X.2017.08.017.

[15]

Bao Z, Xie L, Lu N N, 2019. A Point Cloud Registration Algorithm Using Depth Image. Navigation of China. 42, 2(July. 2019), 8-11. https://dx.doi.org/10.3969/j.issn.1000-4653.2019.02.002.

[16]

Wang M C, Fan Y Y, Chen B G. 2017. Realization of adaptive non-uniformity correction of infrared image based on SOPC. Infrared and Laser Engineering. 46, 6 (July. 2017), 201-206. https://dx.doi.org/10.3788/IRLA201746.0628001.

[17]

Lin W M, Zhang L, Peng X W, 2023. The curative effect of internal heat acupuncture in the treatment of low back myofasciitis and infrared thermal imaging study. Shaanxi Journal of Traditional Chinese Medicine. 44, 1 (February. 2023), 112-114. https://dx.doi.org/10.3969/j.issn.1000-7369.2023.01.027.

[18]

Yang G, Tian Z N, Li H, 2020. Research of target detection of hyperspectral images in complex background based on linear unmixing. Laser Technology. 44, 2 (April. 2020), 143-147. https://dx.doi.org/10.7510/jgjs.issn.1001-3806.2020.02.001.

[19]

Zhang Y C, Hou X Y. 2017. The Temperature Segregation Area of Asphalt Pavement Detection Method Based on Infrared Thermal Images. Acta Metrologica Sinica. 38, 1 (March. 2017), 23-27. https://dx.doi.org/10.3969/j.issn.1000-1158.2017.01.05.

[20]

El-Tawel G S, Helmy A K. 2014. An edge detection scheme based on least squares support vector machine in a contourlet HMT domain. Applied Soft Computing Journal. 26, 1 (October. 2014), 418-427. https://dx.doi.org/10.1016/j.asoc.2014.10.025.

Digital Library

Cited By

Ergene MBayrak ACeylan M(2024)A new deep learning based end-to-end pipeline for hamstring injury detection in thermal images of professional football playerQuantitative InfraRed Thermography Journal10.1080/17686733.2024.2364964(1-18)Online publication date: 5-Jul-2024
https://doi.org/10.1080/17686733.2024.2364964

Index Terms

Research on sports injury recovery detection based on infrared thermography
1. Computing methodologies
  1. Computer graphics
    1. Image compression

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cover image ACM Other conferences

ICDLT '23: Proceedings of the 2023 7th International Conference on Deep Learning Technologies

July 2023

115 pages

ISBN:9798400707520

DOI:10.1145/3613330

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 28 September 2023

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Nanjing Forest Police College Pre-research Program of Grant no:
National College Students' Innovation Program of Grant no:
Fundamental Research Funds for the Central college of Grant no:
National College Students' Entrepreneurship Training Program of Grant no:
Jiangsu University Qing Lan Project of Grant no:

Conference

ICDLT 2023

ICDLT 2023: 2023 7th International Conference on Deep Learning Technologies

July 27 - 29, 2023

Dalian, China

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Cited By

Ergene MBayrak ACeylan M(2024)A new deep learning based end-to-end pipeline for hamstring injury detection in thermal images of professional football playerQuantitative InfraRed Thermography Journal10.1080/17686733.2024.2364964(1-18)Online publication date: 5-Jul-2024
https://doi.org/10.1080/17686733.2024.2364964

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