Development of an artificial intelligence-enabled non-invasive digital stethoscope for monitoring the heart condition of athletes in real-time
DOI:
https://doi.org/10.47197/retos.v60.108633Keywords:
Sports therapy, Physical culture education, Health monitoring technology, Personalized training, Student engagement, Real-time cardiovascular monitoring, AI-enabled stethoscopeAbstract
This study investigates the efficacy of AI-enabled digital stethoscopes in enhancing physical performance, increasing student engagement and motivation, and improving psychological well-being among physical culture students. The experimental design involved two groups of 40 students each: the experimental group used AI-enabled stethoscopes for real-time cardiovascular monitoring, while the control group relied on traditional heart rate monitoring methods. The results indicated significant improvements in physical performance, engagement, and psychological well-being for the experimental group. Real-time monitoring facilitated personalized training adjustments, optimizing training loads and preventing overexertion, leading to superior performance outcomes. Additionally, the use of innovative monitoring tools significantly increased student motivation and engagement in physical culture classes, as reflected in higher attendance rates and more enthusiastic participation. Psychological assessments revealed that continuous health monitoring reduced anxiety levels and enhanced overall mental well-being, providing students with a sense of security and proactive health management. These findings underscore the transformative potential of integrating advanced monitoring technologies into physical education and rehabilitation programs, offering precise, real-time data that supports individualized and responsive interventions. The study concludes with a call for further research to explore the long-term impacts and broader applications of AI-enabled health monitoring tools in diverse educational and clinical settings, aiming to maximize their benefits and improve overall student and patient outcomes.
Keywords: Sports therapy, Physical culture education, Health monitoring technology, Personalized training, Student engagement, Real-time cardiovascular monitoring, AI-enabled stethoscope.
References
S.-H. Sunwoo et al., “Soft bioelectronics for the management of cardiovascular diseases,” Nat. Rev. Bioeng., pp. 1–17, Sep. 2023, doi: https://doi.org/10.1038/s44222-023-00102-z.
Z. Jiang et al., “Automated valvular heart disease detection using heart sound with a deep learning algorithm,” IJC Heart & Vasculature, vol. 51, pp. 101368–101368, Apr. 2024, doi: https://doi.org/10.1016/j.ijcha.2024.101368.
R. Jaros, J. Koutny, M. Ladrova, and R. Martinek, “Novel phonocardiography system for heartbeat detection from vari-ous locations,” Sci. Rep., vol. 13, p. 14392, Sep. 2023, doi: https://doi.org/10.1038/s41598-023-41102-8.
Viktor Avbelj and M. Brloznik, “Phonocardiography and Electrocardiography with a Smartphone,” Sep. 2020, doi: https://doi.org/10.23919/mipro48935.2020.9245211.
Doskarayev, Bauyrzhan, et al. "Development of Computer Vision-enabled Augmented Reality Games to Increase Moti-vation for Sports." International Journal of Advanced Computer Science and Applications 14.4 (2023).
G. Peng, H. Zou, and J. Wang, “Classification of phonocardiograms using residual convolutional neural network and MLP,” Computing in cardiology, Dec. 2022, doi: https://doi.org/10.22489/cinc.2022.001.
Omarov, B., Altayeva, A., Demeuov, A., Tastanov, A., Kassymbekov, Z., & Koishybayev, A. (2020, December). Fuzzy controller for indoor air quality control: a sport complex case study. In International Conference on Advanced In-formatics for Computing Research (pp. 53-61). Singapore: Springer Singapore.
Moez Krichen, “Convolutional Neural Networks: A Survey,” Computers, vol. 12, no. 8, pp. 151–151, Jul. 2023, doi: https://doi.org/10.3390/computers12080151.
X. Zhao, L. Wang, Y. Zhang, X. Han, Muhammet Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif. Intell. Rev., vol. 57, no. 4, Mar. 2024, doi: https://doi.org/10.1007/s10462-024-10721-6.
Omarov, B., Narynov, S., & Zhumanov, Z. (2023). Artificial intelligence-enabled chatbots in mental health: a systematic review. Comput. Mater. Continua 74, 5105–5122 (2022), https://doi.org/10.1109/ACIT50332.2020.9300109.
J. Chen et al., “Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts,” Nat. Commun., vol. 15, no. 1, p. 976, Feb. 2024, doi: https://doi.org/10.1038/s41467-024-44930-y.
P. P. Sengupta, J. Kluin, S.-P. Lee, J. K. Oh, and A. I. P. M. Smits, “The future of valvular heart disease assessment and therapy,” The Lancet, Mar. 2024, doi: https://doi.org/10.1016/s0140-6736(23)02754-x.
M.-H. Guo et al., “Attention mechanisms in computer vision: A survey,” Comput. Vis. Media., Mar. 2022, doi: https://doi.org/10.1007/s41095-022-0271-y.
Vera, C. R., Cámara, I. A., & González-Moro, I. M. (2024). Analysis of the factors of heart rate variability affected after a hypoxia tolerance test as a function of gender. Retos: nuevas tendencias en educación física, deporte y recreación, (55), 177-183.
N. Jatia and K. Veer, “Techniques Used in Phonocardiography: A Review,” Lecture notes in mechanical engineering, pp. 79–90, Jan. 2022, doi: https://doi.org/10.1007/978-981-16-9236-9_8.
AbdelKebir Sabil and Sandrine Launois, “Tracheal Sound Analysis,” Adv. Exp. Med. Biol., pp. 265–280, Jan. 2022, doi: https://doi.org/10.1007/978-3-031-06413-5_16.
F. Vásquez-Iturralde, M. Flores-Calero, F. Grijalva-Arévalo, and Andrés Rosales-Acosta, “Automatic Classification of Cardiac Arrhythmias using Deep Learning Techniques: A Systematic Review,” IEEE Access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3408282.
F. Liu et al., “Advancing brain-inspired computing with Hybrid Neural networks,” Natl. Sci. Rev., Feb. 2024, doi: https://doi.org/10.1093/nsr/nwae066.
S. Lu, M. Liu, L. Yin, Z. Yin, X. Liu, and W. Zheng, “The multi-modal fusion in visual question answering: a review of attention mechanisms,” PeerJ, vol. 9, pp. e1400–e1400, May 2023, doi: https://doi.org/10.7717/peerj-cs.1400.
Sallauddin Mohmmad and Suresh Kumar Sanampudi, “Exploring current research trends in sound event detection: a systematic literature review,” Multimed. Tools Appl., Apr. 2024, doi: https://doi.org/10.1007/s11042-024-18740-9.
Shams Forruque Ahmed et al., “Deep learning modelling techniques: current progress, applications, advantages, and challenges,” Artif. Intell. Rev., vol. 56, Apr. 2023, doi: https://doi.org/10.1007/s10462-023-10466-8.
Z. Wang, Y. Ma, and Y. Zhang, “Review of pixel-level remote sensing image fusion based on deep learning,” Information Fusion, Sep. 2022, doi: https://doi.org/10.1016/j.inffus.2022.09.008.
Sarsenkul Tileubay et al., “Development of Deep Learning Enabled Augmented Reality Framework for Monitoring the Physical Quality Training of Future Trainers-Teachers,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 3, Jan. 2024, doi: https://doi.org/10.14569/ijacsa.2024.0150334.
H. Xiao, L. Li, Q. Liu, X. Zhu, and Q. Zhang, “Transformers in medical image segmentation: A review,” Biomed. Signal Process. Control., vol. 84, p. 104791, Jul. 2023, doi: https://doi.org/10.1016/j.bspc.2023.104791.
J. D. K. Abel, S. Dhanalakshmi, and R. Kumar, “A comprehensive survey on signal processing and machine learning techniques for non-invasive fetal ECG extraction,” Multimed. Tools Appl., Jul. 2022, doi: https://doi.org/10.1007/s11042-022-13391-0.
T. Anbalagan, M. K. Nath, D. Vijayalakshmi, and A. Anbalagan, “Analysis of various techniques for ECG signal in healthcare, past, present, and future,” Biomedical Engineering Advances, vol. 6, p. 100089, Nov. 2023, doi: https://doi.org/10.1016/j.bea.2023.100089.
Z. Chen, M. Ma, T. Li, H. Wang, and C. Li, “Long sequence time-series forecasting with deep learning: A survey,” Information Fusion, p. 101819, Apr. 2023, doi: https://doi.org/10.1016/j.inffus.2023.101819.
Omarov, B., Batyrbekov, A., Suliman, A., Omarov, B., Sabdenbekov, Y., & Aknazarov, S. (2020, November). Electron-ic stethoscope for detecting heart abnormalities in athletes. In 2020 21st International Arab Conference on Infor-mation Technology (ACIT) (pp. 1-5). IEEE, https://doi.org/10.1109/ACIT50332.2020.9300109.
Khani, A. A. M., Soldoozy, A., Rudi, F. S., & Zandi, E. (2024). Improving signal isolation in hybrid RF duplexer utilizing a band-pass filter. Memories-Materials, Devices, Circuits and Systems, 8, 100112.
M. S. Khan et al., “Artificial intelligence and heart failure: A state-of-the-art review,” Eur. J. Heart Fail., vol. 25, no. 9, pp. 1507–1525, Sep. 2023, doi: https://doi.org/10.1002/ejhf.2994.
Álvarez, C., Peñailillo, L., Saavedra, P. I., Tuesta, M., Mayorga, D. J., Domaradski, J., ... & Floody, P. D. (2024). Ex-ercise training is effective for arterial stiffness and blood pressure rehabilitation in hypertensive adults. Retos: nuevas tendencias en educación física, deporte y recreación, (56), 301-311.
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