Research on the influence of health fitness and mental health analyzed by image processing technology
Abstract
Sports offer numerous benefits for physical and mental health, and current research focuses on a comprehensive evaluation of an athlete's well-being to enhance performance. Thermal imaging techniques are employed to observe athletes' activities in both psychological and physiological aspects. During competitive pursuits and exercise, thermal imaging records the athlete's temperature fluctuations. These temperature data, combined with other biometric information like heart rate, provide a more in-depth understanding of the athlete's state. To establish a connection with biomechanical performance, we consider muscle activation patterns and motion dynamics. Muscle activation during intense competition leads to increased metabolic heat, which is detectable via thermal imaging. For example, highly activated muscle groups may exhibit distinct temperature elevations. Motion dynamics, such as the speed and range of limb movements, also impact heat dissipation and distribution. Faster movements may cause more rapid heat convection, altering the thermal patterns captured. The collected thermal images undergo processing to reduce noise and enhance contrast. Specific regions are identified to extract relevant features, which are then analyzed for temperature patterns. Abnormal temperature distributions are detected using Gradient Optimized Recurrent Neural Networks (GO-RNN) to assess the athlete's physical and mental health. This analysis not only predicts potential injuries but also links thermal data with biomechanical performance. By correlating thermal imaging results with muscle activation and motion dynamics, we can develop more accurate performance evaluation models. This integration allows for a more precise understanding of an athlete's condition, enabling personalized training programs and better injury prevention strategies, ultimately leading to improved athletic performance and well-being.
References
1. Håkansson, A., Moesch, K., Jönsson, C., & Kenttä, G. (2021). Potentially prolonged psychological distress from postponed olympic and paralympic games during COVID-19—career uncertainty in elite athletes. International journal of environmental research and public health, 18(1), 2.
2. Gorczynski, P., Currie, A., Gibson, K., Gouttebarge, V., Hainline, B., Castaldelli-Maia, J. M., ... & Swartz, L. (2021). Developing mental health literacy and cultural competence in elite sport. Journal of Applied Sport Psychology, 33(4), 387-401.
3. Facer-Childs, E. R., Hoffman, D., Tran, J. N., Drummond, S. P., & Rajaratnam, S. M. (2021). Sleep and mental health in athletes during COVID-19 lockdown. Sleep, 44(5), zsaa261.
4. Wang, T (Wang, Tong); Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis. npj Digit Med 2022 Sep 15;5(1):145. doi: 10.1038/s41746-022-00692-9
5. Savoli, A., Bhatt, M. (2022). Chronic Patients’ Emotions toward Self-managing Care IT: The Role of Health Centrality and Dependence on IT. Journal of Organizational and End User Computing(forthcoming).
6. Zajda, J., & Vissing, Y. (Eds.). (2023). Globalisation, human rights, sports, and culture (Vol. 37). Springer Nature.
7. Chang, C., Putukian, M., Aerni, G., Diamond, A., Hong, G., Ingram, Y., ... & Wolanin, A. (2020). Mental health issues and psychological factors in athletes: detection, management, effect on performance and prevention: American Medical Society for Sports Medicine Position Statement—Executive Summary. British journal of sports medicine, 54(4), 216-220.
8. Purcell, R., Gwyther, K., & Rice, S. M. (2019). Mental health in elite athletes: increased awareness requires an early intervention framework to respond to athlete needs. Sports medicine-open, 5(1), 46.
9. Zhen, H., Kumar, P. M., & Samuel, R. D. J. (2021). Internet of things framework in athletics physical teaching system and health monitoring. International Journal on Artificial Intelligence Tools, 30(06n08), 2140016.
10. Jeong, H., Lee, J. Y., Lee, K., Kang, Y. J., Kim, J. T., Avila, R., ... & Rogers, J. A. (2021). Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients. Science advances, 7(20), eabg3092.
11. Song, J., Li, Y., Guo, X., Shen, K.N., and Ju, X. (2022). Making Mobile Health Information Advice Persuasive: An Elaboration Likelihood Model Perspective. Journal of Organizational and End User Computing(forthcoming).
12. Zheng, S., Chang, P-Y., Chen, J., Chang, Y-W. and Fan, H-C. (2022). An Investigation of Patients’ Decisions to Use eHealth: A View of Multichannel Services. Journal of Organizational and End User Computing(forthcoming).
13. Sang, M., Kim, K., Shin, J., & Yu, K. J. (2022). Ultra‐Thin Flexible Encapsulating Materials for Soft Bio‐Integrated Electronics. Advanced Science, 9(30), 2202980.
14. Hillen, B., López, D. A., Schömer, E., Nägele, M., & Simon, P. (2022). Towards exercise radiomics: deep neural network-based automatic analysis of thermal images captured during exercise. IEEE Journal of Biomedical and Health Informatics, 26(9), 4530-4540.
15. Trejo-Chavez, O., Amezquita-Sanchez, J. P., Huerta-Rosales, J. R., Morales-Hernandez, L. A., Cruz-Albarran, I. A., & Valtierra-Rodriguez, M. (2022). Automatic Knee Injury Identification through Thermal Image Processing and Convolutional Neural Networks. Electronics, 11(23), 3987.
16. Aylwin, P. E., Racinais, S., Bermon, S., Lloyd, A., Hodder, S., & Havenith, G. (2021). The use of infrared thermography for the dynamic measurement of skin temperature of moving athletes during competition; methodological issues. Physiological Measurement, 42(8), 084004.
17. Liu, S. B., Xie, B. K., Yuan, R. Y., Zhang, M. X., Xu, J. C., Li, L., & Wang, Q. H. (2023). Deep learning enables parallel camera with enhanced-resolution and computational zoom imaging. PhotoniX, 4(1), 1-20.
18. Yang, B., Li, X., Hou, Y., Meier, A., Cheng, X., Choi, J. H., ... & Li, H. (2020). Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses-a review. Energy and Buildings, 224, 110261.
19. Chen, J., Samuel, R. D. J., & Poovendran, P. (2021). LSTM with bio inspired algorithm for action recognition in sports videos. Image and Vision Computing, 112, 104214.
20. Fu, B., & Fu, X. (2022). Distributed simulation system for Athletes’ mental health in the internet of things environment. Computational Intelligence and Neuroscience, 2022.
21. Xu, W., Li, Q., & Wang, Y. (2022). Radial Neural Network Processing Applied to Athlete’s Personalized Psychological Regulation Detection System. BioMed Research International, 2022.
22. Chen, S., Guo, L., Xiao, R., Ran, J., Li, H., & Reynoso, L. C. (2023). Establishing a cognitive evaluation model for injury risk assessment in athletes using RBF neural networks. Soft Computing, 1-16.
23. Lu, L., Xi, Y., & Zhang, X. (2023). Application of mobile sensors based on deep neural networks in sports psychological detection and prevention. Preventive medicine, 173, 107613.
24. Nazarov, A. M. (2020). Psychological Mechanism For Improvement Of Psychological Protection Of Sportsmen. Theoretical & Applied Science, (1), 435-438.
25. Cao, X., Zhao, X., Tang, H., Fan, N., & Zereg, F. (2023). Football players’ strength training method using image processing based on machine learning. Plos one, 18(6), e0287433.
26. Glandorf, H. L., Madigan, D. J., Kavanagh, O., & Mallinson-Howard, S. H. (2023). Mental and physical health outcomes of burnout in athletes: a systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 1-45.
27. Benítez-Sillero, J. D. D., Martínez-Aranda, L. M., Sanz-Matesanz, M., & Domínguez-Escribano, M. (2021). Determining factors of psychological performance and differences among age categories in youth football players. Sustainability, 13(14), 7713.
28. Eke, A., Adam, M., Kowalski, K., & Ferguson, L. (2020). Narratives of adolescent women athletes’ body self-compassion, performance, and emotional well-being. Qualitative Research in Sport, Exercise and Health, 12(2), 175-191.
29. Liu., M, & Qiao, E. (2023). Analysis and design of dual-feature fusion neural network for sports injury estimation model. Neural Computing and Applications, 35(20), 14627-14639.
30. He, Y. (2021). Athlete human behavior recognition based on continuous image deep learning and sensors. Wireless Networks, 1-12.
31. Rubin, E., & Beuk, F. (2021). Emotions and Spillover Effects of Social Networks Affective Well Being. Journal of Organizational and End User Computing (JOEUC), 33(5), 1-24. http://doi.org/10.4018/JOEUC.20210901.oa1
32. R. Gade, A. Jørgensen and T.B. Moeslund: Long-term Occupancy Analysis using Graph-Based Optimisation in Thermal Imagery. IEEE Conference on Computer Vision and Pattern Recognition 2013.
33. Herzog W, Schappacher-Tilp G. Molecular mechanisms of muscle contraction: A historical perspective. J Biomech. 2023; 155:111659. doi: 10.1016/j.jbiomech.2023.111659
34. Leonard TR, DuVall M, Herzog W. Force enhancement following stretch in a single sarcomere. Am J Physiol Cell Physiol. 2010; 299(6):C1398-C1401. doi:10.1152/ajpcell.00222.2010
35. Barrett JM, Malakoutian M, Fels S, Brown SHM, Oxland TR. Muscle short-range stiffness behaves like a maxwell element, not a spring: Implications for joint stability. PLoS One. 2024; 19(8): e0307977. Published 2024 Aug 14. doi: 10.1371/journal.pone.0307977
36. Rassier DE. Sarcomere mechanics in striated muscles: from molecules to sarcomeres to cells. Am J Physiol Cell Physiol. 2017; 313(2):C134-C145. doi:10.1152/ajpcell.00050.2017
37. Furrer R, Handschin C. Molecular aspects of the exercise response and training adaptation in skeletal muscle. Free Radic Biol Med. 2024; 223:53-68. doi: 10.1016/j.freeradbiomed.2024.07.026
38. Normand-Gravier T, Solsona R, Dablainville V, et al. Effects of thermal interventions on skeletal muscle adaptations and regeneration: perspectives on epigenetics: a narrative review. Eur J Appl Physiol. Published online November 28, 2024. doi:10.1007/s00421-024-05642-9
39. Thomas ACQ, Stead CA, Burniston JG, Phillips SM. Exercise-specific adaptations in human skeletal muscle: Molecular mechanisms of making muscles fit and mighty. Free Radic Biol Med. 2024; 223:341-356. doi: 10.1016/j.freeradbiomed.2024.08.010
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
