Sistem Deteksi dan Klasifikasi Pergerakan Kepala Menggunakan K-Nearest Neighbor - Nikmatus Soleha; Dahnial Syauqy; Eko Setiawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
The development of technology by utilizing the movement of the human body is developing very rapidly. One of them is an artificial intelligence system that utilizes head movements to control wheelchairs based on embedded systems. In that study, the head moved as a control to move a wheelchair. Previous similar research have raised several obstacles, one of which is the system is inflexible so that not all users can use it. To overcome these problems, researchers conducted a system design by calibrating the system so that it could classify the direction of head movement according to its class using the K-Nearest Neighbor (K-NN) method. With this, it is hoped that the system can be used more flexibly by users. The system uses MPU6050 sensor and ESP32 microcontroller which are arranged in the form of a headband. The results obtained from the system classification calibration are displayed on the serial monitor in the form of head movement class. The system testing was carried out with four experiments in each head movement class, there were five head movement classes tested. Based on the test results on this system, obtained an accuracy rate of 95% of the K-Nearest Neighbor classification.