Open Access

ARTICLE

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

by Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

1 Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
2 Electrical Engineering Department, Universitas Jenderal Soedirman, Purbalingga, 53371, Indonesia

* Corresponding Author: Azhari Azhari. Email:

(This article belongs to the Special Issue: Artificial Neural Networks and its Applications)

Computers, Materials & Continua 2024, 81(2), 3079-3096. https://doi.org/10.32604/cmc.2024.058193

Received 06 September 2024; Accepted 18 October 2024; Issue published 18 November 2024

Abstract

Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but struggle with the complexity of fast-paced sports like badminton. We proposed an ensemble learning model combining support vector machines (SVM), logistic regression (LR), random forest (RF), and adaptive boosting (AdaBoost) for badminton action recognition. The data in this study consist of video recordings of badminton stroke techniques, which have been extracted into spatiotemporal data. The three-dimensional distance between each skeleton point and the right hip represents the spatial features. The temporal features are the results of Fast Dynamic Time Warping (FDTW) calculations applied to 15 frames of each video sequence. The weighted ensemble model employs soft voting classifiers from SVM, LR, RF, and AdaBoost to enhance the accuracy of badminton action recognition. The E2 ensemble model, which combines SVM, LR, and AdaBoost, achieves the highest accuracy of 95.38%.

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Keywords

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