Apply Machine-Learning Model for Clustering Rowing Players

doi:10.1145/3638837.3638872

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 會議論文 > Item 987654321/125337

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125337

Title:	Apply Machine-Learning Model for Clustering Rowing Players
Authors:	Wilaikaew, Patcharawit;Noisriphan, Watchara;Chen, Chien-chang;Charoensuk, Jirawan;Ruengitinun, Somchoke;Chootong, Chalothon
Date:	2024-03-07
Issue Date:	2024-03-15 12:05:47 (UTC+8)
Abstract:	Rowing, as a sport composed of single player or multiple players, performs body movements under certain rhythm with slight variation. The analysis of rhythm alternation or match is important on rowing research and merit our study. Therefore, this study analyzes the rowing movements by the following three procedures, rowing cycle segmentation, feature extraction, rowing cycle clustering. The rowing cycle segmentation procedure segments each player's video to videos of single cycle under the analysis of MediaPipe detected joint points. The feature extraction procedure calculates features from each rowing cycle by selecting amplitudes, angles, angular speeds of 4 selected joint points. At last, the rowing cycle clustering procedure analyzes all one-cycled videos using above features by different clustering and scoring methods. Three clustering methods, including K-means, Birch, and Gaussian-mixture, are experimented in this study for finding the most efficient one. A hybrid measurement from the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index, is proposed for finding the optimal clusters number. Experimental results of 15 players’ videos show that applying K-means clustering algorithm with the proposed hybrid measurement performs better for finding the rowing group.
Relation:	ICNCC '23: Proceedings of the 2023 12th International Conference on Networks, Communication and Computing
DOI:	10.1145/3638837.3638872
Appears in Collections:	[資訊工程學系暨研究所] 會議論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	4	View/Open

Loading...