Klasifikasi Gerakan Jari Tangan Berdasarkan Sinyal Electromyogram Pada Lengan

Electromyogram (EMG) signal is data that shows movement activity in muscles. By mapping the pattern between the shape of the EMG signal and the movement of the finger, other applications can be developed such as motion detection in healthy people as well as rehabilitation patients. Many studies had been conducted to map the relationship between EMG signals and finger movements, one of which is the relationship between the number of data acquisition channels used and the complexity of the system. The number of channels used is proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires bigger resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square (RMS) is applied in a sliding window as feature extraction. The classifier used is the artificial neural network (ANN). System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively.