TY - GEN
T1 - Deep Learning Algorithms for Recognition of Badminton Strokes
T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
AU - Isa, Wan Hasbullah Mohd
AU - Abdullah, Muhammad Amirul
AU - Razman, Mohd Azraai Mohd
AU - Majeed, Anwar P.P.Abdul
AU - Khairuddin, Ismail Mohd
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In this study, three deep learning algorithms were used to classify three badminton strokes: forehand drive, forehand clear, and smash. The traditional manual methods of stroke recognition were time-consuming and error-prone, while the deep learning algorithms provided a faster and more accurate analysis of the various strokes in real time. To capture the players’ motions from off-court angles, the study recommended a video capture method. The research evaluated the performance of several deep learning models, including simple dense neural network (SDNN), recurrent neural network (RNN), and RNN with an additional gated recurrent unit (GRU) layer (RNN-GRU), using OpenCV programming and the MediaPipe keypoints library for feature extraction. The dataset was split into an 80:20 ratio for training and validation, and 300 shot videos were collected for each stroke to evaluate the accuracy and losses of each model. Coaches and players can benefit from the study's outcomes by gaining more objective insights into the game, allowing them to develop more effective strategies.
AB - In this study, three deep learning algorithms were used to classify three badminton strokes: forehand drive, forehand clear, and smash. The traditional manual methods of stroke recognition were time-consuming and error-prone, while the deep learning algorithms provided a faster and more accurate analysis of the various strokes in real time. To capture the players’ motions from off-court angles, the study recommended a video capture method. The research evaluated the performance of several deep learning models, including simple dense neural network (SDNN), recurrent neural network (RNN), and RNN with an additional gated recurrent unit (GRU) layer (RNN-GRU), using OpenCV programming and the MediaPipe keypoints library for feature extraction. The dataset was split into an 80:20 ratio for training and validation, and 300 shot videos were collected for each stroke to evaluate the accuracy and losses of each model. Coaches and players can benefit from the study's outcomes by gaining more objective insights into the game, allowing them to develop more effective strategies.
KW - Badminton
KW - Classification
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85187805777&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8498-5_5
DO - 10.1007/978-981-99-8498-5_5
M3 - Conference Proceeding
AN - SCOPUS:85187805777
SN - 9789819984978
T3 - Lecture Notes in Networks and Systems
SP - 53
EP - 60
BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
A2 - Tan, Andrew
A2 - Zhu, Fan
A2 - Jiang, Haochuan
A2 - Mostafa, Kazi
A2 - Yap, Eng Hwa
A2 - Chen, Leo
A2 - Olule, Lillian J. A.
A2 - Myung, Hyun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2023 through 23 August 2023
ER -