Pemilihan Fitur Statistik serta Implementasi Model Decision Tree Machine Learning Pada Arduino Nano 33 BLE Untuk Pendeteksian dan Klasifikasi Gerak Jatuh dan Kecenderungan Jatuh Lansia Berbasis Nilai Akselerasi
Abstract
Orang yang berusia di atas 60 tahun dianggap sudah lanjut usia. Penurunan fungsi fisiologis terkait usia, termasuk fungsi tulang dan otot, berkontribusi terhadap peningkatan risiko jatuh pada lansia. Salah satu keadaan yang bisa berakibat fatal adalah terjatuh. Kemunduran berbagai proses organ yang terlibat dalam menjaga keseimbangan tubuh dapat dipengaruhi oleh dampak ini, yang mungkin berdampak pada kematian. Orang tua banyak jatuh sekarang, namun keluarga sering tidak menyadari keberadaan mereka. Secara tradisional, tetangga di dekat rumah lansia adalah sumber utama informasi mengenai kondisi mereka. Studi sebelumnya telah menggunakan sifat statistik dan sensor inersia untuk mengenali aktivitas manusia pada orang tua. Dalam studi ini, kami akan mengevaluasi metode ekstraksi fitur dan pembelajaran mesin terbaik. Variabel maximum, minimum, mean, median, kurtosis, skewness, dan variance yang dikumpulkan dari data sensor akselerometer menggunakan sensor IMU akan diperiksa untuk ekstraksi fitur menggunakan metode Fast Fourier Transform. Digunakan Cross-validation untuk mengetahui performa mode Decision Tree. Dengan nilai akurasi 99,8% dan nilai ekstraksi ciri terbaik pada Maksimum accelX, median accelZ, variance accelZ, variance Magnitude, dan maksimum Magnitude.
Kata kunci—Elderly, Fall, Machine Learning, Accelero, Micromlgen, Kodular
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