Sistem Pengenalan Pergerakan Lengan Menggunakan Exponential Moving Average Dengan Metode Decision Tree Berbasis EMG - Aufa Nizar Faiz; Rizal Maulana; Fitri Utaminingrum (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Humans can carry out their work in a healthy condition, so health is the most important thing in life. But many people are unable to do their jobs due to physical limitations, commonly called persons with disabilities. Biomedical engineering is medical science that uses medical science and design engineers to solve health problems. Electromyograhy (EMG), one of the biomedical sciences that can detect signals generated by contractions in muscles, using EMG can make the system of detecting signals of muscle contractions, especially in the arm muscles. This system will help detect arm muscle contractions for people with disabilities in the arms. Detection is performed on changes in the degree of the arm, the degrees detected are 0, 30, 60, 90, 120, 150, and 180 degrees. Signals received by EMG have noise that can interfere with detection, so signal refining is required in the form of an exponential moving average (EMA) method. Exponential moving average has a weighting value to make a reward, the value used is 0.1 and 0.3. After refining the signal, the detection of degree changes is performed using the decision tree classification method. Then the results of the classification will be displayed on the LED and LCD.