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  3. วิทยาลัยวิศวกรรมศาสตร์ COLLEGE OF ENGINEERING
  4. Eng-ECE-M-Thesis
Please use this identifier to cite or link to this item: https://rsuir-library.rsu.ac.th/handle/123456789/3162
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dc.contributor.advisorRong Phoophuangpairoj-
dc.contributor.authorMing Qi-
dc.date.accessioned2026-01-28T02:44:54Z-
dc.date.available2026-01-28T02:44:54Z-
dc.date.issued2024-
dc.identifier.urihttps://rsuir-library.rsu.ac.th/handle/123456789/3162-
dc.descriptionThesis (M. Eng. (Electrical and Computer Engineering)) -- Rangsit University, 2024en_US
dc.language.isoenen_US
dc.publisherRangsit University. Libraryen_US
dc.subjectWeight trainingen_US
dc.subjectMachine learningen_US
dc.subjectWeight lifting -- Coachingen_US
dc.titleDetermining success in snatch weightlifting using pose landmarks and barbell detectionen_US
dc.typeThesisen_US
dc.description.other-abstractThis 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 weightliftingen_US
dc.description.degree-nameMaster of Engineeringen_US
dc.description.degree-levelMaster's Degreeen_US
dc.contributor.degree-disciplineElectrical and Computer Engineeringen_US
Appears in Collections:Eng-ECE-M-Thesis

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