Please use this identifier to cite or link to this item:
https://rsuir-library.rsu.ac.th/handle/123456789/3162| Title: | Determining success in snatch weightlifting using pose landmarks and barbell detection |
| Authors: | Ming Qi |
| metadata.dc.contributor.advisor: | Rong Phoophuangpairoj |
| Keywords: | Weight training;Machine learning;Weight lifting -- Coaching |
| Issue Date: | 2024 |
| Publisher: | Rangsit University. Library |
| metadata.dc.description.other-abstract: | This research integrated computer vision and machine learning techniques to objectively evaluate snatch weightlifting success. By leveraging MediaPipe for skeletal detection and You Only Look Once (YOLO) object detection for barbell detection, the study classified snatch into six phases. An artificial neural network (ANN) and support vector machine (SVM) were applied to classify weightlifting phases from features extracted using MediaPipe. The distances between an athlete’s hands and a barbell were computed using the MediaPipe features, which represented the points on an athlete’s right and left hands as well as the points on a barbell. This study employed different methods to evaluate weightlifting success. For example, the method that used the holding period of the sixth phase could obtain a 95% accuracy rate, whereas the method that evaluated the presence of all six phases in sequence could derive a lower accuracy of 70%. A method that evaluated the ordered six phases, the holding time of the sixth phase, and the barbell slipping achieved the highest accuracy rate of 100%. The proposed method, which did not require specialized equipment, could achieve notable weightlift phase classification and efficiently determine the success or failure of snatch weightlifting |
| Description: | Thesis (M. Eng. (Electrical and Computer Engineering)) -- Rangsit University, 2024 |
| metadata.dc.description.degree-name: | Master of Engineering |
| metadata.dc.description.degree-level: | Master's Degree |
| metadata.dc.contributor.degree-discipline: | Electrical and Computer Engineering |
| URI: | https://rsuir-library.rsu.ac.th/handle/123456789/3162 |
| metadata.dc.type: | Thesis |
| Appears in Collections: | Eng-ECE-M-Thesis |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| MING QI.pdf | 1.19 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.