The effect of real-time pose recognition on badminton learning performance

ABSTRACT

Badminton is a very popular subject in Physical Education (PE). Many students enroll badminton courses in every semester which pose a tremendous teaching load to the instructors. The one-on-one guiding/feedback time provided by the instructor to each student is also greatly reduced. To overcome this challenge, some studies have tried to adopt pose recognition technique in teaching badminton. However, the lack of mobility and recognition accuracy problems hinder its applicability. To address this issue, a new pose recognition technique, OpenPose, was employed to develop a real-time pose recognition badminton teaching APP in this study. The APP was then installed on a mobile device to enhance badminton smash skill learning performance. The scores distribution of the experimental group shows majority of students achieved satisfactory performance, this implies the badminton teaching APP is helpful to all students no matter what their initial skill levels are.

KEYWORDS:

• Pose recognition
• badminton teaching
• motor skill learning
• real-time feedback
• physical education

Acknowledgments

The author would like to thank Ya-Tien Liu for participating in the algorithm design. This research was supported by the Ministry of Science and Technology, Taiwan under project numbers MOST-108-2410-H-110 -055,MOST-109-2511-H-003-053-MY3, MOST-108-2511-H-003-061-MY3, and 107-2511-H-003-054-MY3.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Kuo-Chin Lin

Kuo-Chin Lin is an associate professor at the Center for Physical and Health Education, Si Wan College, National Sun Yat-sen University (NSYSU) in Taiwan. His sports specialty is badminton teaching and training, and his research interest is in educational technology.

Cheng-Wen Ko

Cheng-Wen Ko is currently an associate professor at the Department of Computer Science and Engineering, National Sun Yat-sen University, Taiwan. Her research interest is focused on the technical development and improvement of advanced MR imaging and biomedical imaging.

Hui-Chun Hung

Hui-Chun Hung is an assistant professor in the Graduate Institute of Network Learning Technology, National Central University. He received his Ph.D. from the Institute of Information Systems and Applications, National Tsing Hua University, Taiwan, in 2015. His research interests concentrate on educational data mining, learning analytics, flipped learning, technology-assisted learning, and visual analytics. His research presented a holistic perspective to explore the effectiveness of integrating open educational resources, mobile technologies, educational data mining, and machine learning.

Nian-Shing Chen

Nian-Shing Chen is chair professor in the Institute for Research Excellence in Learning Sciences and Program of Learning Sciences at the National Taiwan Normal University, Taiwan. His current research interests include assessing e-Learning course performance; online synchronous teaching & learning; mobile & ubiquitous learning; gesture-based learning and educational robotics. Professor Chen is a golden core member of IEEE, ACM and the former Chair of the IEEE Technical Committee on Learning Technology.