Deteksi Gerakan Tangan menggunakan Support Vector Machine pada Dumbbell Berbasis Raspberry Pi Zero

ERWANI MERRY SARTIKA, AAN DARMAWAN, WILLIAM EKA JAYA, ELIZABETH WIANTO

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ABSTRAK

Olahraga yang dilakukan di mana saja, dapat menggunakan alat sederhana seperti dumbbell. Latihan yang dilakukan harus sesuai usia dan kondisi kesehatan secara umum. Sebuah sistem diperlukan untuk mendeteksi gerakan sehingga membantu pemakai dalam menggunakan dumbbell. Dumbbell dilengkapi oleh sensor Inertial Measurement Unit (IMU), single board computer Raspberry Pi Zero W, dan LED RGB. Gerakan akan dideteksi oleh sensor Inertial Measurement Unit (IMU) yang dikirim ke Raspberry Pi Zero W untuk dilakukan preprocessing data. Algoritma Support Vector Machine (SVM) digunakan untuk mendapatkan model pendeteksi gerakan olahraga pada dumbbell. Bila gerakan terdeteksi maka Raspberry Pi Zero W akan memberi perintah LED RGB untuk menyalakan warna tertentu sesuai dengan deteksi gerakan. Berdasarkan hasil uji coba yang dilakukan kepada 5 orang dengan gerakan yang dideteksi sebanyak 6 gerakan, dumbbell pendeteksi gerakan memiliki tingkat keberhasilan sebesar 90%-94%.

Kata kunci: dumbbell, deteksi gerakan, Raspberry Pi Zero W, SVM

 

ABSTRACT

Exercises that can be done anywhere, can use simple tools such as dumbbells. Exercises should be appropriate for age and general health conditions. A system is needed to detect motion so as to assist the wearer in using the dumbbells. The dumbbell is equipped with an Inertial Measurement Unit (IMU) sensor, a single board computer Raspberry Pi Zero W, and RGB LEDs. Movement will be detected by the Inertial Measurement Unit (IMU) sensor which is sent to the Raspberry Pi Zero W for preprocessing the data. The Support Vector Machine (SVM) algorithm is used to obtain a sports motion detection model on dumbbells. If motion is detected, the Raspberry Pi Zero W will give an RGB LED command to turn on certain colors according to motion detection. Based on the results of trials conducted on 5 people with 6 movements detected, motion detection dumbbells have a success rate of 90%-94%.

Keywords: dumbbell, motion detection, Raspberry Pi Zero W, SVM


Kata Kunci


dumbbell; deteksi gerakan; Raspberry Pi Zero W; SVM

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v10i1.105

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