Penerapan Algoritma Gradient Boosting pada Sinyal EEG sebagai Pengendali Kursi Roda

GERARLDO INDRA DARMAWAN, ERWANI MERRY SARTIKA, ERIC CHANDRA, NOVIE THERESIA BR. PASARIBU, HERI ANDRIANTO

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ABSTRAK

Berdasarkan data Badan Penduduk Statistik (BPS) tahun 2019, jumlah penduduk lanjut usia yaitu 23,4 juta dan 26,2% diantaranya mengalami keluhan kesehatan. Beberapa keluhan kesehatan yang dialami berkaitan dengan mobilitas. Kursi roda merupakan salah satu alat bantu yang kerap digunakan oleh penyandang disabilitas atau seseorang yang memiliki keterbatasan mobilitas. Brain-Computer Interface digunakan sebagai sistem kendali kursi roda menggunakan Raspberry Pi berdasarkan masukan berupa sinyal EEG. Sinyal EEG tersebut digunakan untuk memprediksi perintah otak dan rangsangan gerakan bola mata dengan menerapkan algoritma gradient boosting. Hasil prediksi machine learning merupakan set point untuk menjalankan motor DC sehingga kursi roda dapat bergerak berdasarkan hasil prediksi. Sistem BCI pada kursi roda telah dilakukan uji coba, integrasi BCI pada kursi roda berhasil diterapkan dengan persentase keberhasilan sebesar 60%.

Kata kunci: BCI, Machine Learning, Wheelchair Control.

 

ABSTRACT

According to the Badan Penduduk Statistik 2019, the number of elderly population is 23.4 million, and 26.2% of them experience health complaints. Some of these complaints are related to mobility. Wheelchairs are one of the commonly used aids for people with disabilities or mobility limitations. The Brain-Computer Interface (BCI) is employed as a control system for a wheelchair, utilizing a Raspberry Pi, which operates based on input signals derived from EEG (Electroencephalogram) signals. These EEG signals are used to predict brain commands and stimulate eye movement through the application of gradient boosting algorithms. The machine learning prediction results are the set points to run the DC motor so that the wheelchair can move based on the prediction results. The BCI system for wheelchairs has been tested, and the integration of BCI into wheelchairs has been successfully applied with a 60% success rate.

Keywords: BCI, Machine Learning, Wheelchair Control.


Kata Kunci


BCI; Machine Learning; Wheelchair Control

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v12i2.541

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