Penentuan Learning Rate Terbaik CNN Pada Pengenalan Individu Berbasis Analisis Gait
DOI:
https://doi.org/10.33633/joins.v8i1.7806Keywords:
gait event detection, penentuan learning rate, convolutional neural network, pola trayektoriAbstract
Trayektori tubuh manusia untuk analisis gait tidak terbatas pada kondisi permukaan medan yang rata. Hal ini berpengaruh pada analisis gait untuk penelitian pengenalan identitas individu yang terkait dengan kondisi medan yang dilalui. Pergelangan kaki menjadi bagian tubuh yang berkontribusi pada trayektori tubuh manusia terhadap medan yang dilalui melalui dua kondisi yaitu Heel-Strike (HS) dan Toe-Off (TO). HS dan TO memiliki pola trayektori yang saling berbeda untuk setiap individu sehingga membutuhkan penentuan parameter learning rate yang tepat. Penentuan learning rate terbaik merupakan salah satu langkah penting dalam menghasilkan pengenalan identitas individu terbaik. Pada kegiatan penelitian ini, data yang digunakan adalah data berformat C3D yang direkam melalui perangkat motion capture dengan skenario berjalan lurus (WS/Walking Straight) oleh enam orang sebagai partisipan. Penentuan learning rate terbaik menggunakan metode convolutional neural network (CNN) dengan pretrain pembanding adalah ResNet18 dan ResNet50. Percobaan yang dilakukan menghasilkan performa terbaik diperoleh ResNet18 baik pada pengukuran Average Position (AP) maupun pendeteksian kondisi HS dan TO.References
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