Computer-assisted self-training for kyudo posture rectification using computer vision methods

Wardah Farrukh, Dustin van der Haar

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


To some individuals, particularly archery students, perfecting the art of Kyudo is of utmost importance. These devoted students are always trying to correct their posture because it plays a significant role in effectively shooting at the target. However, due to the lack of attention from instructors, students are often forced to train on their own without any guidance. It is difficult for students to analyze their own faults because the shoulders, hips, and feet should be in line with another, parallel to the floor and straight to the target. The proposed solution is, therefore, a system that aims to assist students in correcting their posture. The system will classify the technique presented by the user and using PoseNet, the system will output coordinates and draw a skeleton structure of the user’s technique along with the instructor’s technique. The coordinates will then be measured for similarity and appropriate feedback is provided to the user. The results for classification, using CNN and SVM showed an accuracy of 81.25% and 80.2%, respectively. The results indicate the feasibility of the approach, however, improvement is required in certain areas. Recommendations for improving the approach are discussed.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
Publication statusPublished - 2021

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


  • Computer vision
  • Convolutional Neural Network
  • PoseNet
  • Support vector machine

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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