Machine Learning-Based Classification of Badminton Strokes Using IMU-Integrated Racket
DOI:
https://doi.org/10.15282/mekatronika.v7i1.12004Keywords:
Badminton, Machine Learning, Classification, IMUAbstract
Badminton, a fast-paced racket sport, demands precision, power, and strategic execution. While previous research has explored using inertial measurement units (IMUs) for stroke classification, this study presents an integration of sensor-embedded rackets with IMUs to provide a more accurate and automated approach. The collected motion data was processed and classified using machine learning models such as logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). Of these, k-NN achieved the highest accuracy at 75 percent, with backhand strokes showing better classification precision. However, challenges in classifying forehand strokes suggest a need for further refinement in feature extraction. The study contributes a framework for improving stroke classification accuracy in badminton and offers insights into optimizing ML-driven motion analysis for more precise performance assessment in badminton.
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