Machine learning-based approach for evaluating physical fitness through motion detection
DOI:
https://doi.org/10.35335/mandiri.v14i1.406Keywords:
Exercise classification, Machine learning, Motion detection, Physical fitness, Pose estimationAbstract
Physical fitness assessment is crucial for evaluating an individual's physical performance and endurance. However, traditional methods often rely on manual observation, which can lead to subjectivity and inconsistent results. This study proposes a machine learning-based approach for physical fitness evaluation through motion detection using pose estimation and exercise classification models. A quantitative method was employed to train and evaluate models for four exercise types: push-ups, sit-ups, pull-ups, and chinning. Each model was trained separately and assessed using accuracy, precision, recall, and F1-score metrics, achieving accuracies of 97.50% for push-ups, 97.67% for sit-ups, 97.00% for pull-ups, and 98.50% for chinning. The maximum error margin compared to manual counting was 2.48%. System-generated outputs were validated against manual observations using standard evaluation matrices. These findings indicate that machine learning can offer a reliable, consistent, and automated solution for physical fitness assessment, with the potential to enhance training programs, support remote fitness monitoring, and reduce human error in performance evaluation.
References
Abdul Muthalib, M., Irfan, I., Kartika, K., & Selamat Meliala, S. M. (2023). Pengiraan pose model manusia pada repetisi kebugaran AI pemograman Python berbasis komputerisasi. Infotech Journal, 9(1), 11–19. https://doi.org/10.31949/infotech.v9i1.4233
Arlin, R., & Munir, R. (n.d.). The development of push up counter Android application with computer vision.
Asrese, A. S., Eravuchira, S. J., Bajpai, V., Sarolahti, P., & Ott, J. (2019). Measuring web latency and rendering performance: Method, tools, and longitudinal dataset. IEEE Transactions on Network and Service Management, 16(2), 535–549. https://doi.org/10.1109/TNSM.2019.2896710
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Durden, J. M., Hosking, B., Bett, B. J., Cline, D., & Ruhl, H. A. (2021). Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance. Progress in Oceanography, 196, 102612. https://doi.org/10.1016/j.pocean.2021.102612
Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. https://doi.org/10.1016/j.engappai.2022.105151
Guo, M. H., Xu, T. X., Liu, J. J., Liu, Z. N., Jiang, P. T., Mu, T. J., Zhang, S. H., Martin, R. R., Cheng, M. M., & Hu, S. M. (2022). Attention mechanisms in computer vision: A survey. Computational Visual Media, 8(3), 331–368. https://doi.org/10.1007/s41095-022-0271-y
Huang, Y., & Wang, W. (2022). Cultural creativity, industrial scale, management methods, and their roles in rural revitalization from the perspective of big data. Mathematical Problems in Engineering, 2022, 1–9. https://doi.org/10.1155/2022/6792716
Kim, J. W., Choi, J. Y., Ha, E. J., & Choi, J. H. (2023). Human pose estimation using MediaPipe pose and optimization method based on a humanoid model. Applied Sciences, 13(4), 2700. https://doi.org/10.3390/app13042700
Kumar, R., Singh, S. K., Bajpai, A., & Sinha, A. (n.d.). MediaPipe and CNNs for real-time ASL gesture recognition.
Kusno Hadi, I., Manajemen Pertahanan Akademi Militer, P., Basuki, A., Raindra, M., & Ferdiyansyah, M. I. (2024). Pengelolaan manajemen pembinaan Garjas Pasipamops dalam meningkatkan nilai tes kesegaran jasmani sersan taruna di Batalyon Taruna Dewasa. Jurnal Pertahanan, 11(2), 2355–5262.
Marpaung, N. L., & Al Amzah, R. (2022). Rancang bangun program aplikasi tes kesegaran jasmani Indonesia berbasis Android. Jurnal Teknik Informatika dan Sistem Informasi, 9(2). http://jurnal.mdp.ac.id
McAllister, P., Zheng, H., Bond, R., & Moorhead, A. (2018). Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets. Computers in Biology and Medicine, 95, 217–233. https://doi.org/10.1016/j.compbiomed.2018.02.008
Miftahul, A. F., Fatkhu R, I., & Maliki, O. (2020). Upaya peningkatan hasil belajar psikomotorik lompat jauh gaya jongkok melalui drill dan MediaPipe jump hanging ball pada siswa kelas VII SMPN 1 Kandeman. JSSF, 6(1). http://journal.unnes.ac.id/sju/index.php/jssf
Mugiat, D. (n.d.). Pengaruh metode circuit training berbantuan alat peraga, tanpa berbantuan alat peraga, dan motivasi terhadap hasil tes kesegaran jasmani siswa TNI AL.
Nurudin, M., Jayanti, W., Saputro, R. D., Saputra, M. P., & Yulianti, D. (2019). Pengujian black box pada aplikasi penjualan berbasis web menggunakan teknik boundary value analysis. Jurnal Informatika, 4(4), 2622–4615. http://openjournal.unpam.ac.id/index.php/informatika
Sánchez-Vicinaiz, T. J., Camacho-Pérez, E., Castillo-Atoche, A. A., Cruz-Fernandez, M., García-Martínez, J. R., & Rodríguez-Reséndiz, J. (2024). MediaPipe frame and convolutional neural networks-based fingerspelling detection in Mexican sign language. Technologies, 12(8). https://doi.org/10.3390/technologies12080124
Satrya Perbawa, D., & Nurohim, G. S. (2020). Pengujian aplikasi berbasis website dengan black box testing metode boundary value analysis dan responsive testing. Journal SPEED, 12(4).
Shin, J., Matsuoka, A., Hasan, M. A. M., & Srizon, A. Y. (2021). American Sign Language alphabet recognition by extracting features from hand pose estimation. Sensors, 21(17), 5810. https://doi.org/10.3390/s21175810
Tian, Y. (2020). Artificial intelligence image recognition method based on convolutional neural network algorithm. IEEE Access, 8, 125731–125744. https://doi.org/10.1109/ACCESS.2020.3006097
Verma, A. R., Singh, G., Meghwal, K., Ramji, B., & Dadheech, P. K. (2024). Enhancing sign language detection through MediaPipe and convolutional neural networks (CNN). arXiv preprint. http://arxiv.org/abs/2406.03729
Wu, J. (2017). Introduction to convolutional neural networks. https://cs.nju.edu.cn/wujx/paper/CNN.pdf
Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C. L., & Grundmann, M. (2020). MediaPipe hands: On-device real-time hand tracking. arXiv preprint arXiv:2006.10214. https://arxiv.org/abs/2006.10214
Zhang, Y. (2022). Applications of Google MediaPipe pose estimation using a single camera.
Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
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Copyright (c) 2025 M. Fazil Rais, Chadafa Zulti Noorta, M. Ilham AlFatrah, H.A Danang Rimbawa, Uvi Desi Fatmawati
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