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Taekwondo pose estimation with deep learning architectures on one dimensional and two dimensional data

This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Longterm Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. | Journal of Computer Science and Cybernetics V.39 N.4 2023 343 368 DOI no. 10.15625 1813-9663 18043 TAEKWONDO POSE ESTIMATION WITH DEEP LEARNING ARCHITECTURES ON ONE-DIMENSIONAL AND TWO-DIMENSIONAL DATA DAT TIEN NGUYEN CHAU NGOC HA HA THANH THI HOANG TRUONG NHAT NGUYEN TUYET NGOC HUYNH HAI THANH NGUYEN College of Information and Communication Technology Can Tho University Can Tho Viet Nam Abstract. Practicing sports is an activity that helps people maintain and improve their health enhance memory and concentration reduce anxiety and stress and train teamwork and leadership ability. With the development of science and technology artificial intelligence in sports has become increasingly popular with the public and brings many benefits. In particular many applications help people track and evaluate athletes achievements in competitions. This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group FFMPEG technique using MoveNet. After that we use deep learning architectures such as Long Short-Term Memory Networks Convolutional Long Short-Term Memory and Long- term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second we use sequence images to train using video classification architecture. Finally we recognize poses in sports lessons using skeleton data to remove noise in the image such as background and extraneous objects behind the exerciser. As a result our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson. Keywords. Pose classification Skeleton Sports lessons Taekwondo. 1. INTRODUCTION The emergence of information technology has brought many positive e ects on educa- tion development. The development of information technology especially the Internet has