Penerapan Estimasi Pose dengan Model MoveNet untuk Klasifikasi Posisi Gerakan Deadlift Menggunakan XGBoost

Authors

  • Kevin Ragil Krisna Dyansyah Universitas Nusantara PGRI Kediri Indonesia
  • Ahmad Bagus Setiawan Universitas Nusantara PGRI Kediri Indonesia
  • Patmi Kasih Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/9xzamk35

Abstract

Klasifikasi fase gerakan pada latihan deadlift penting untuk menghindari cedera akibat postur yang salah. Penelitian ini mengembangkan sistem klasifikasi posisi “up” dan “down” menggunakan estimasi pose berbasis MoveNet Lightning dan algoritma XGBoost. Sistem mendeteksi 17 keypoint tubuh secara real-time melalui browser dan menghitung tiga sudut utama (pinggul, lutut, punggung) sebagai fitur klasifikasi. Data sudut tersebut dilabeli otomatis dan dilatih menggunakan XGBoost dengan akurasi uji mencapai 98,51%. Analisis menunjukkan bahwa sudut lutut memiliki kontribusi tertinggi terhadap hasil klasifikasi. Sistem berjalan sepenuhnya di sisi klien menggunakan React.js dan TensorFlow.js, memungkinkan klasifikasi real-time tanpa backend. Pendekatan ini menunjukkan bahwa kombinasi model ringan dan algoritma efisien dapat menghasilkan sistem pelatih digital yang akurat, ringan, dan portabel. 

Keywords:

deadlift, Pose Estimation, movenet, Machine Learning, XGBoost

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References

[1] TensorFlow Hub, “MoveNet: Ultra fast and accurate pose detection model,” https://www.tensorflow.org/hub/tutorials/movenet.

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[3] A. ÇELİKEL and I. A. OZKAN, “Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe,” International Journal of Applied Methods in Electronics and Computers, Sep. 2024, doi: 10.58190/ijamec.2024.107.

[4] C. Arrowsmith, D. Burns, T. Mak, M. Hardisty, and C. Whyne, “Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning,” Sensors, vol. 23, no. 1, p. 363, Dec. 2022, doi: 10.3390/s23010363.

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[8] W. Wahid, A. A. AlArfaj, E. A. Alabdulqader, T. Sadiq, H. Rahman, and A. Jalal, “Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier,” IEEE Access, vol. 12, pp. 179839–179856, 2024, doi: 10.1109/ACCESS.2024.3498093.

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Published

2025-07-10

Issue

Vol. 9 No. 1 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

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Copyright (c) 2025 Kevin Ragil Krisna Dyansyah, Ahmad Bagus Setiawan, Patmi Kasih

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How to Cite

Penerapan Estimasi Pose dengan Model MoveNet untuk Klasifikasi Posisi Gerakan Deadlift Menggunakan XGBoost. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(1), 368-377. https://doi.org/10.29407/9xzamk35