Smart sensors represent a significant advancement in modern sports science, and their effective use enhances the ability to monitor and analyze athlete performance in real time. The integration of these sensors has enhanced the accuracy of data collection related to physical activity, biomechanics, and physiological responses, thus providing valuable insights for performance optimization, injury prevention, and rehabilitation. This paper provides an overview of the research progress in the application of smart sensors in the field of sports science; highlights the current advances, challenges, and future directions in the deployment of smart sensor technologies; and anticipates their transformative impact on sports science and athlete development.

Aluminum alloys with low-weight property are promising structure materials for sports equipment. Alloying element-rich second-phase particles create the risk of localized corrosion and result in failure of sports equipment. Chromate conversion coatings as conventional and successful surface treatments were employed to provide a thin but compact film against corrosion. However, chromate species were toxic and carcinogenic for human beings and this process has been highly restricted. In this sense, alternative processes such as trivalent chromium conversion coating with low environmental risk require better corrosion-resistant performance compared to chromate conversion coating. In addition, the closed-loop system of the chromate electroplating process has been used in Europe and the United States. This is also a sustainable process for surface treatment of aluminum alloys applied in sports equipment. The present paper aims to summarize the methods and types of different aluminum alloy surface treatments and compiles the effects of various surface treatments on the corrosion resistance of aluminum alloys. The eco-friendly application of aluminum alloys in the field of sports equipment may be facilitated in the future.

The Hawkeye system was regarded as a successful and effective referee assistant in commercial applications. However, the high hardware investment and strong professionalism set up a physical hurdle in public application. In this sense, one AI referee assistance system is a small-scale system developed under the guidance of computer vision theory and technology, using deep learning-based object detection algorithms and computer graphics-related knowledge. Its main function is to use computer vision technology to make correct and fair judgments on controversial decisions in badminton matches. The AI referee assistance system uses the Harmony OS platform to build a frontend mini-program and uses the YOLOV5 algorithm to detect and track corresponding targets, ultimately achieving judgments both inside and outside the bounds as well as serving violations. In this study, it was found that the object detection model trained in this study performed well, with a prediction accuracy of up to 99%. For posture recognition, this study also utilizes the relatively mature MediaPipe algorithm, which ensures high accuracy of the entire intelligent referee system’s ruling and meets the needs of current students. This helps college students better participate in badminton training and competitions, further improving their enthusiasm for participating in sports and, to some extent, solving the problem of scarce referee resources.