Apply Machine-Learning Model for Clustering Rowing Players
學年 112
學期 1
發表日期 2023-12-15
作品名稱 Apply Machine-Learning Model for Clustering Rowing Players
作品名稱（其他語言）
著者 Patcharawit Wilaikaew; Watchara Noisriphan; Chen, Chien-chang; Jirawan Charoensuk; Somchoke Ruengitinun; Chalothon Chootong
作品所屬單位
出版者
會議名稱 2023 The 12th International Conference on Networks, Communication and Computing (ICNCC)
會議地點 Osaka, Japan
摘要 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.
關鍵字
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20231215~20231217
通訊作者
國別 JPN
公開徵稿
出版型式
出處 ICNCC '23: Proceedings of the 2023 12th International Conference on Networks, Communication and Computing
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125337 )