Machine Learning-Based Classification of Table Tennis Swings Using Racket Kinematics
Image credit: [AI]Abstract
This report investigates the application of machine learning models to classify the demographics of table swings based on racket kinematics data, with a strong focus on predicting a combined age and gender label. The Data Set originally sourced from DRYAD has slightly been modified for the purpose of this report. We apply and compare three supervised classification algorithms, K-Nearest Neighbours (KNN), Support Vector Machines (SVM) and One-vs-Rest Logistic Regression, evaluating their performance using known evaluation metrics. Our findings provide insights into the discriminative power of racket motion features and the behaviour of various classifiers on a real-word problem.
Type
This work is driven by the results in my previous paper on LLMs.
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