1. ISTCMTM
  2. All Volumes and Issues
  3. Volume 85, no.2, 2024
  4. DESIGN AND DEVELOPMENT OF AI CLOUD-BASED VIDEO RECORDING SYSTEM FOR ATHLETE MOVEMENTS

DESIGN AND DEVELOPMENT OF AI CLOUD-BASED VIDEO RECORDING SYSTEM FOR ATHLETE MOVEMENTS

ISTCMTM.
2024;
Volume 85(2), Number 2
: 13-20
https://doi.org/10.23939/istcmtm2024.02.013
Authors:
  1. Yevhen Bershchankyi
  2. Halyna Klym
1
Lviv Polytechnic National University
2
Lviv Politechnic National University

This paper presents the design and development of an AI cloud-based video recording system for athlete move- ment analysis. The proposed system utilizes the Wemos D1 Mini microcontroller as the core hardware platform and a GoPro cam- era for high-quality video capture. By leveraging the capabilities of these components, the system enables real-time video re- cording of athlete movements, facilitating detailed performance analysis and feedback. Furthermore, the system seamlessly inte- grates with Amazon Web Services (AWS) IoT Core, enabling efficient data transmission and storage in the cloud. Through re- search and development, both the hardware and software components of the system were designed and implemented, ensuring robust performance and scalability. Experiments demonstrate the efficacy of the proposed solution in capturing high-fidelity video footage of athlete movements and securely transmitting it to the cloud for further analysis. This research lays the foundation for advanced athlete monitoring systems, offering valuable insights for coaches, trainers, and sports scientists to enhance training regimens and optimize performance.

Sports engineering
video recording system
AI movement analysis
Wemos D1 Mini
Internet of Things.
  1. Chase, C. (2020). The Data Revolution: Cloud Com- puting, Artificial Intelligence, and Machine Learning in the Future of Sports. In: Schmidt, S.L. (eds) 21st Century Sports. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030- 50801-2_10
  2. Chaudhary, S., Bhargave, V., Kulkarni, S., Puranik, P., & Shinde, A. (2018). Home Automation System Using WeMos D1 Mini. Int. Res. Journ. Engineering and Technology (IRJET) Vol, 5, 4238-4241. [Online]. Available: https://www.academia.edu/download/ 58283614/IRJET-V5I5944.pdf
  3. Crandall AS, Mamolo S, Morgan M. SkiMon: A Wireless Body Area Network for Monitoring Ski Flex and Motion during Skiing Sports. Sensors. 2022; 22(18):6882. https://doi.org/10.3390/s22186882
  4. Bershchanskyi, Y. and Klym, H., 2023, October. Information System for Administration of Medical Institution. In 2023 13th Int. Conf. on Depend. Sys- tems, Services and Techn. (DESSERT) (pp. 1-4). IEEE. https://doi.org/10.1109/DESSERT61349.2023. 10416537
  5. B.C. Kavitha, R. Vallikannu, IoT based intelligent industry monitoring system, in 2019 6th International Conference on Signal Processing and Integrated Net- works  (SPIN),  IEEE,  2019,  p.  63–65. https://doi.org/10.1109/SPIN.2019.8711597
  6. Jaya, N.I. and Hossain, M.F., 2018, October. A proto- type air flow control system for home automation us- ing mqtt over websocket in aws iot core. In 2018 In- ternational Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (pp. 111-1116). IEEE. https://doi.org/10.1109/CyberC.2018.00032
  7. Dineva, Kristina, and Tatiana Atanasova. "Design of scalable IoT architecture based on AWS for smart livestock." Animals 11, no.9 (2021): 2697. https://doi.org/10.3390/ani11092697
  8. Dineva, K.; Atanasova, T. Security in IoT Systems. In Proceedings of the XIX International Multidiscipli- nary Scientific GeoConference SGEM, Vienna, Aus- tria, 9–12 December 2019; Volume 19, p. 576–577. https://doi.org/10.3390/ani11092697
  9. Jangid, N.K. and Gupta, M.K., 2022, June. Protecting software design in the cloud using AWS IoT. In Pro- ceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, p. 561-562. https://doi.org/10.1145/3498361.3538784
  10. Hakkı, S.O.Y., 2021. ESP8266 and ESP32 series of SoC microcontrollers. Programmable Smart Microcon- troller Cards, 110. [Online]. Available: https://www. isres.org/books/Programlanabilir Akıllı Mikrodenetley- ici Kartlar_01_16-12-2021.pdf#page=110
  11. Chakrabarti, A., Sadhu, P.K. & Pal, P. AWS IoT Core and Amazon DeepAR based predictive real-time monitoring framework for industrial induction heating systems. Microsyst. Technol 29, 441–456 (2023). https://doi.org/10.1007/s00542-022-05311-
  12. Sharma, V. (2022, April). Object detection and recogni- tion using Amazon Rekognition with Boto3. In 2022 6th Int. Conf. on Trends in Electronics and Informatics (pp. 727-732). IEEE. https://doi.org/10.1109/ ICOEI53556.2022.9776884