Enhanced Badminton Stroke Recognition Using Hybrid RGB–Skeleton Features and Ensemble Learning
Received: 16 October 2025 | Revised: 12 November 2025 | Accepted: 29 November 2025 | Online: 9 February 2026
Corresponding author: Azhari Azhari
Abstract
Previous studies on automatic recognition of badminton strokes have shown persistent gaps, particularly uneven classification accuracy across stroke categories and suboptimal overall performance. To overcome these limitations, this study proposes a hybrid framework that combines handcrafted spatiotemporal features with a two-stage feature selection and weighted ensemble learning. From representative video frames, RGB-based descriptors (Histogram of Oriented Gradients (HOG), Histogram of Optical Flow (HOF), Motion Boundary Histogram (MBH)) and skeleton-based features (Range of Motion Index (ROMI) for spatial and Dynamic Time Warping (DTW) for temporal) are extracted. Dimensionality reduction is applied through Autoencoder compression followed by SelectKBest to preserve the most informative features. The refined hybrid features are then classified using a weighted soft voting ensemble that integrates Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Adaptive Boosting (AdaBoost) to enhance both accuracy and class-wise balance. Experimental evaluation on a badminton stroke dataset demonstrates the effectiveness of this approach, achieving 98.21% accuracy. The results highlight that the best-performing feature combination is ROMI, DTW, and HOF, confirming that hybrid handcrafted features with ensemble Machine Learning (ML) significantly improve robustness and stability, offering strong potential for practical applications in performance analysis, training systems, and sport analytics.
Keywords:
badminton stroke, hybrid, spatiotemporal, feature selection, voting classifier, ensemble learningDownloads
References
N. Gupta and B. B. Agarwal, "Recognition of Suspicious Human Activity in Video Surveillance: A Review," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10529–10534, Apr. 2023. DOI: https://doi.org/10.48084/etasr.5739
S. A. Alshammari and N. S. Albalawi, "Enhancing Healthcare Monitoring: A Deep Learning Approach to Human Activity Recognition using Wearable Sensors," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18843–18848, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9255
C. Ma, D. Yu, and H. Feng, "Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model," Journal of Healthcare Engineering, vol. 2021, no. 1, Oct. 2021, Art. no. 7892902. DOI: https://doi.org/10.1155/2021/7892902
I. Ghosh, S. Ramasamy Ramamurthy, A. Chakma, and N. Roy, "DeCoach: Deep Learning-based Coaching for Badminton Player Assessment," Pervasive and Mobile Computing, vol. 83, July 2022, Art. no. 101608. DOI: https://doi.org/10.1016/j.pmcj.2022.101608
J. Yang, Z. Shi, and Z. Wu, "Vision-based action recognition of construction workers using dense trajectories," Advanced Engineering Informatics, vol. 30, no. 3, pp. 327–336, Aug. 2016. DOI: https://doi.org/10.1016/j.aei.2016.04.009
H. Y. Ting, K. S. Sim, and F. S. Abas, "Automatic Badminton Action Recognition Using RGB-D Sensor," in 3rd International Conference on Key Engineering Materials and Computer Science, Singapore, 2014, vol. 1042, pp. 89–93. DOI: https://doi.org/10.4028/www.scientific.net/AMR.1042.89
T. Steels, B. Van Herbruggen, J. Fontaine, T. De Pessemier, D. Plets, and E. De Poorter, "Badminton Activity Recognition Using Accelerometer Data," Sensors, vol. 20, no. 17, Sept. 2020, Art. no. 4685. DOI: https://doi.org/10.3390/s20174685
N. F. Ghazali, N. Shahar, and M. A. As’Ari, "Badminton Strokes Recognition using Inertial Sensor and Machine Learning Approach," in 2022 2nd International Conference on Intelligent Cybernetics Technology & Applications, Bandung, Indonesia, 2022, pp. 1–5. DOI: https://doi.org/10.1109/ICICyTA57421.2022.10037897
B. Purnama, B. Erfianto, and I. R. Wirawan, "Time Series Classification of Badminton Pose using LSTM with Landmark Tracking," Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 1, pp. 27–37, Nov. 2024. DOI: https://doi.org/10.35882/jeeemi.v7i1.488
Y. Wang, W. Fang, J. Ma, X. Li, and A. Zhong, "Automatic Badminton Action Recognition Using CNN with Adaptive Feature Extraction on Sensor Data," in 15th International Conference on Intelligent Computing, Nanchang, China, 2019, pp. 131–143. DOI: https://doi.org/10.1007/978-3-030-26763-6_13
J. Liu and B. Liang, "An Action Recognition Technology for Badminton Players Using Deep Learning," Mobile Information Systems, vol. 2022, no. 1, May 2022, Art. no. 3413584. DOI: https://doi.org/10.1155/2022/3413584
Q. Li, T.-C. Chiu, H.-W. Huang, M.-T. Sun, and W.-S. Ku, "VideoBadminton: A Video Dataset for Badminton Action Recognition," in 2024 IEEE International Conference on Big Data, Washington, DC, USA, 2024, pp. 1387–1392. DOI: https://doi.org/10.1109/BigData62323.2024.10825009
X. Zhu, L. Liu, J. Huang, G. Chen, X. Ling, and Y. Chen, "The analysis of motion recognition model for badminton player movements using machine learning," Scientific Reports, vol. 15, no. 1, May 2025, Art. no. 19030. DOI: https://doi.org/10.1038/s41598-025-02771-9
K. Berahmand, F. Daneshfar, M. Rahmaninia, M. Haghighat, and M. Jalili, "A Comprehensive Survey on Multi-View Classification: Methods, Applications, and Challenges," ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 6, Nov. 2025, Art. no. 144. DOI: https://doi.org/10.1145/3767728
H. H. S. Junaid, F. Daneshfar, and M. A. Mohammad, "Automatic colorectal cancer detection using machine learning and deep learning based on feature selection in histopathological images," Biomedical Signal Processing and Control, vol. 107, Sept. 2025, Art. no. 107866. DOI: https://doi.org/10.1016/j.bspc.2025.107866
F. Asriani, A. Azhari, and W. Wahyono, "Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model," Computers, Materials & Continua, vol. 81, no. 2, pp. 3079–3096, Nov. 2024. DOI: https://doi.org/10.32604/cmc.2024.058193
R. Yue, Z. Tian, and S. Du, "Action recognition based on RGB and skeleton data sets: A survey," Neurocomputing, vol. 512, pp. 287–306, Nov. 2022. DOI: https://doi.org/10.1016/j.neucom.2022.09.071
K. Berahmand, F. Daneshfar, E. S. Salehi, Y. Li, and Y. Xu, "Autoencoders and their applications in machine learning: a survey," Artificial Intelligence Review, vol. 57, no. 2, Feb. 2024, Art. no. 28. DOI: https://doi.org/10.1007/s10462-023-10662-6
R. Nair and A. Bhagat, "Feature Selection Method To Improve The Accuracy of Classification Algorithm," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6, pp. 124–127, Apr. 2019.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sept. 1995. DOI: https://doi.org/10.1023/A:1022627411411
C.-Y. J. Peng, K. L. Lee, and G. M. Ingersoll, "An Introduction to Logistic Regression Analysis and Reporting," The Journal of Educational Research, vol. 96, no. 1, pp. 3–14, Sept. 2002. DOI: https://doi.org/10.1080/00220670209598786
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001. DOI: https://doi.org/10.1023/A:1010933404324
M. S. Toor et al., "An Optimized Weighted-Voting-Based Ensemble Learning Approach for Fake News Classification," Mathematics, vol. 13, no. 3, Feb. 2025, Art. no. 449. DOI: https://doi.org/10.3390/math13030449
Z. Liang and T. E. Nyamasvisva, "Badminton Action Classification Based on Human Skeleton Data Extracted by AlphaPose," in 2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence, Xi’an, China, 2023, pp. 1–4. DOI: https://doi.org/10.1109/ICSMD60522.2023.10490491
Md. A. I. Anik, M. Hassan, H. Mahmud, and Md. K. Hasan, "Activity recognition of a badminton game through accelerometer and gyroscope," in 2016 19th International Conference on Computer and Information Technology, Dhaka, Bangladesh, 2016, pp. 213–217. DOI: https://doi.org/10.1109/ICCITECHN.2016.7860197
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