Penggunaan Computer Vision untuk Estimasi Pose Squat sebagai Solusi Alternatif Latihan Kebugaran di Gym
DOI:
https://doi.org/10.29407/fpt1hh30Keywords:
computer vision, mediapipe, opencv, estimasi pose, squat, latihan gymAbstract
Penelitian ini bertujuan untuk memberikan solusi alternatif dalam memastikan form gerakan squat tetap benar melalui teknologi Computer Vision berbasis Python menggunakan MediaPipe dan OpenCV. Latar belakang penelitian ini adalah pentingnya form yang tepat saat melakukan latihan squat untuk mencegah cedera dan memaksimalkan efektivitas latihan, terutama bagi individu yang tidak memiliki akses ke gym trainer. Metode penelitian melibatkan pengembangan sistem estimasi pose yang mendeteksi titik-titik kunci tubuh (landmark) menggunakan MediaPipe, serta perhitungan sudut untuk mengevaluasi form squat secara real-time. Dataset gerakan squat dikumpulkan untuk menguji akurasi sistem. Hasil penelitian menunjukkan bahwa sistem ini mampu mendeteksi kesalahan form squat dan memberikan umpan balik secara efektif dengan akurasi tinggi. Diskusi menekankan manfaat teknologi ini dalam menyediakan solusi pelatihan yang efisien, terjangkau, Hasil pengujian menunjukkan bahwa sistem memiliki performa yang sangat baik dengan akurasi sebesar 100%, presisi 100%, recall 100%, dan F1-score 100%.. Kesimpulannya, implementasi Computer Vision melalui MediaPipe dan OpenCV menawarkan inovasi signifikan dalam bidang latihan kebugaran mandiri dengan teknologi yang mudah diterapkan dan digunakan.
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Copyright (c) 2025 Kevin Ragil Krisna Dyansyah, Septian Dwi Purwantoro, Musthofa Ilmi, Resty Wulanningrum
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