Pengenalan Gerakan Olahraga (Push Up dan Sit Up) menggunakan Mediapipe
DOI:
https://doi.org/10.21460/jutei.2026.101.477Keywords:
Workout Movement Recognition, Image Processing, Mediapipe, Push-up, Sit-upAbstract
Motion in sports workouts plays a crucial role in evaluation and learning. This design aims to develop a sports workout movement recognition device based on image processing with high accuracy. The design methodology involves acquiring image data of workout movements and training a recognition model using Mediapipe, which is a deep learning-based object detection algorithm. Image data of workout movements is obtained from push-up and sit-up exercises performed in front of the Sports Movement Acquisition module. The training process of the Mediapipe model utilizes this input data to recognize movements such as push-ups and sit-ups. Initial experimental results indicate that the system can recognize workout movements with satisfactory accuracy. Furthermore, this research includes testing the system with varying distances to determine its accuracy at different ranges. In further experiments, the system can be refined or improved to achieve higher accuracy. This research contributes significantly to the development of sports workout movement recognition technology using the Mediapipe algorithm, focusing on the classification of push-up and sit-up movements. The system can be used in training, evaluation, and assessment of specific workout movements, namely push-ups and sit-ups. The integration of image processing and deep learning in this field holds potential for further development in movement analysis and training for both athletes and beginners. By combining modern technology and sports science, this research opens new opportunities for understanding and enhancing workout movements, particularly in push-up and sit-up exercises, potentially providing significant benefits in athlete training and the development of workout sports.
References
[1] UNICEF, “Landscape Analysis of Overweight and Obesity in Indonesia,” 2021. Accessed: Sep. 25, 2023. [Online]. Available: https://www.unicef.org/indonesia/media/15481/file/Landscape%20analysis%20of%20overweight%20and%20obesity%20in%20Indonesia.pdf
[2] M. A. Elmagd, “Benefits, need and importance of daily exercise,” International Journal of Physical Education, Sports and Health, vol. 3, no. 5, pp. 22–27, 2016, [Online]. Available: https://www.researchgate.net/publication/306118434
[3] ` B. Schoenfeld et al., “Resistance Training Recommendations to Maximize Muscle Hypertrophy in an Athletic Population: Position Stand of the IUSCA,” International Journal of Strength and Conditioning, vol. 1, no. 1, Aug. 2021, doi: 10.47206/ijsc.v1i1.81.
[4] E. Rahmat, A. Rusdiana, and Y. Ruhayati, “PENGEMBANGAN TEKNOLOGI TES CHIN UP BERBASIS ARDUINO UNO DAN SENSOR INFRARED DENGAN LCD DISPLAY,” Jurnal Terapan Ilmu Keolahragaan, vol. 2, no. 1, p. 14, May 2017, doi:10.17509/jtikor.v2i1.4961.
[5] K. Skawinski, F. M. Roca, and R. Dieter, “Workout Type Recognition and Repetition Counting with CNNs from 3D Acceleration Sensed on the Chest.”
[6] S. Shinde, A. Kothari, and V. Gupta, “YOLO based Human Action Recognition and Localization,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 831 838. doi: 10.1016/j.procs.2018.07.112.
[7] J. S. Charlie, M. Wulandari, and Nurwijayanti, “Classification of fertilizer using OpenCV based on color characteristic,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Dec. 2020. doi: 10.1088/1757 899X/1007/1/012053.
[8] V. Tyagi, Understanding Digital Image Processing. CRC Press, 2018. doi: 10.1201/9781315123905.
[9] M. van Heel, G. Harauz, E. V. Orlova, R. Schmidt, dan M. Schatz. (Jan. 1996). A New Generation of the IMAGIC Image Processing System. Journal of Structural Biology. [online]. 116(1), hal. 17–24. Tersedia: https://pubmed.ncbi.nlm.nih.gov/8742718/
[10] J. Ali, “SISTEM SECURITY WEBCAM DENGAN MENGGUNAKAN MICROSOFT VISUAL BASIC (6.0),” Jurnal Teknologi dan Sistem Informasi UNIVRAB, vol. 1, no. 2, 2016.
[11] B. N. Soliev dan N. Gʻiyosiddinov. (2024). Optimizing PyQt5 Development with Qt Designer. Fergana Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi. [online]. Tersedia: -
[12] Gary Bradski, “The OpenCV Library,” Dr. Dobb’s Journal, 2000.
[13] N. Mahamkali, V. Ayyasamy, M. Naveenkumar, and A. Vadivel, “OpenCV for Computer Vision Applications,” 2015. [Online]. Available: https://www.researchgate.net/publication/301590571
[14] Google, “MediaPipe Pose,” https://github.com/google/mediapipe/tree/master.
[15] J. W. Kim, J. Y. Choi, E. J. Ha, and J. H. Choi, “Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model,” Applied Sciences (Switzerland), vol. 13, no. 4, Feb. 2023, doi: 10.3390/app13042700.
[16] J. Kerr and B. Warsaw, “Pickle - Python Object Serialization,” https://github.com/python/cpython/blob/3.10/Doc/library/pickle.rst
[17] NumFOCUS, “About Pandas,” https://pandas.pydata.org/about/index.html.
[18] A. Moryossef et al., “Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.10166
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Andrew Hendisutio, Azis Dzaffin Alfarabi, Wahidin Wahab, Meirista Wulandari
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish articles in JUTEI agree on the following rules:
1. The author grants non exclusive royalty free rights, and is willing to publish articles online and complete (full access). With such rights JUTEI reserves the right to save, transfers, manages in various forms, maintains and publishes articles while keeping the author's name as the copyright owner.
2. Each author contained in the article has contributed fully to the substance and intellectual, and is accountable to the public. If in the future there is a copyright infringement notification then this will be responsibility of the author, not JUTEI.
