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All Journal TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika eProceedings of Engineering
Kahpiasa, Azzahra Nadya
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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PENGEMBANGAN PENGENALAN AKTIVITAS MANUSIA UNTUK LANSIA BERDASARKAN NILAI AKSELEROMETER DAN FITUR STATISTIK Kahpiasa, Azzahra Nadya; Istiqomah, Istiqomah; Mukhtar, Husneni
TEKTRIKA Vol 8 No 1 (2023): TEKTRIKA Vol.8 No.1 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i1.6669

Abstract

Elderly people are those who are 60 years of age or older. Elderly people are more likely to fall due to age-related reductions in physiological processes, particularly bone and muscular functions. Falling is one of the symptoms that can be lethal. These effects may cause mortality if prompt medical assistance is not received due to the deterioration in numerous organ functions required to maintain body homeostasis. Previous studies have tested the Random Forest model from acceleration and gyroscope measurements to identify human activity. In this research, feature extraction was carried out utilizing variables such as maximum, minimum, mean, median, kurtosis, skewness, and variance that were obtained from the Acceleration data from accelerometer sensor in type IMU LSM9DS1. The Acceleration data include Acceleration X, Acceleration Y, Acceleration Z and Acceleration Magnitude. To evaluate the effectiveness of the Decision Tree model, cross-validation will be employed. The best feature extraction values were Magnitude Acceleration Variance, X Acceleration Maximum, Magnitude Acceleration Maximum, Z Acceleration Median, and Z Acceleration Variance, with a Decision Tree model accuracy rate of 99.8%. Key Words: Elderly, Fall, Tendency to Fall, Decision Tree, Statistical Feature
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 Kahpiasa, Azzahra Nadya; Istiqomah, Istiqomah; Mukhtar, Husneni
eProceedings of Engineering Vol. 11 No. 1 (2024): Februari 2024
Publisher : eProceedings of Engineering

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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
Co-Authors Husneni Mukhtar istiqomah istiqomah