Jingjing Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jingjing Zhao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bo Leng
College of Education, Beijing Sports University, Beijing 100091, China
2 shared publications - Jiantao Qi
College of New Energy, China University of Petroleum (East China), Qingdao 266580, China
2 shared publications - Chang Zhu
School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China, Chengdu 611731, China
1 shared publication - Yongqiang Zhu
College of New Energy, China University of Petroleum (East China), Qingdao 266580, China
1 shared publication - Yulong Yang
College of New Energy, China University of Petroleum (East China), Qingdao 266580, China
1 shared publication
Latest Publications
- Open Access
Design and Development of a Public AI Referee Assistance System Based on Harmony OS Platform
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.
Open AccessDesign and Development of a Public AI Referee Assistance System Based on Harmony OS Platform
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.
- Open Access
Research Progress on Applying Intelligent Sensors in Sports Science
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.
Open AccessResearch Progress on Applying Intelligent Sensors in Sports Science
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.