English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62572/95237 (66%)
Visitors : 2545089      Online Users : 335
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    機構典藏 > Office of Physical Education > Journal Article >  Item 987654321/124548
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124548

    Title: Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network
    Authors: Chien-Chang Chen 1, Cheng-Shian Lin 1,*, Yen-Ting Chen 1 , Wen-Her Chen 2 , Chien-Hua Chen 2,3 and I-Cheng Chen 2
    Keywords: OpenPose;graph neural network
    Date: 2023-08-31
    Issue Date: 2023-09-19 12:05:25 (UTC+8)
    Abstract: Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience. The aim of our work is to use a graph-matching network to analyze the similarity in rowers’ rowing posture and further pair rowers to improve the performance of their rowing team. This study proposed a novel video-based performance analysis system to analyze paired rowers using a graph-matching network. The proposed system first detected human joint points, as acquired from the OpenPose system, and then the graph embedding model and graph-matching network model were applied to analyze similarities in rowing postures between paired rowers. When analyzing the postures of the paired rowers, the proposed system detected the same starting point of their rowing postures to achieve more accurate pairing results. Finally, variations in the similarities were displayed using the proposed time-period similarity processing. The experimental results show that the proposed time-period similarity processing of the 2D graph-embedding model (GEM) had the best pairing results.
    Relation: J. Imaging 2023, 9(9), 181
    DOI: 10.3390/jimaging9090181
    Appears in Collections:[Office of Physical Education] Journal Article

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback