Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137983
Title: Automated tool for swimmer analysis
Authors: Soh, Jun Feng
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2020
Publisher: Nanyang Technological University
Project: SCSE19-0117
Abstract: In this paper, an evaluation of existing swimming analysis methodologies was conducted to determine their strengths and weaknesses. Some of the strengths include the ability to maintain a swimmer’s mobility and receiving the guidance of a coach. With the aim to build towards the benefits of existing products, an open source human pose network was adopted to detect human pose based on video frames. Designs were then implemented to identify bad swim strokes from the human pose detected. Suggestions will then be provided to correct the swimmers. This tool was then evaluated to determine the effectiveness. It was concluded that due to inaccuracies with the network, the swim analysis tool did not work as well as intended. However, the concept of implementing a coach’s guidance into the tool is possible, whereas the network requires further evaluation and possibly training on underwater datasets.
URI: https://hdl.handle.net/10356/137983
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Report.pdf
  Restricted Access
2.99 MBAdobe PDFView/Open

Page view(s)

153
Updated on Dec 7, 2022

Download(s)

8
Updated on Dec 7, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.