Examining the Effectiveness of k-Means Clustering using Minkowski Distances on Spatial Data of Tennis Serve Pose for Sports Players to Maintain Good Health

This research paper investigates the effectiveness of K-means clustering using Minkowski distances on spatial data derived from tennis serve poses. The study leverages advanced pose estimation techniques to extract key body angles from video footage of tennis players, focusing on the serve stance. By employing K-means clustering, we aim to classify serve poses into distinct clusters based on joint angles, such as shoulder, elbow, hip, knee, and ankle angles. The Minkowski distance metric is utilized to measure the similarity between data points and cluster centroids, providing a robust framework for analyzing the spatial distribution of serve poses. The results demonstrate that K-means clustering with Minkowski distances effectively categorizes serve poses, offering valuable insights into the biomechanics of tennis serves. This approach has significant implications for coaching and player performance enhancement, enabling the identification of optimal serve stances and deviations from ideal postures.