Identifikasi Ekstraksi Fitur untuk Gerakan Tangan dalam Bahasa Isyarat (SIBI) Menggunakan Sensor MYO Armband - Angga Rahagiyanto (Universitas Bumigora, 2019)
Indonesian: Indonesian SIBI has been widely reviewed by researchers using different types of cameras and sensors. The ultimate goal is to produce a strong, fast and accurate movement recognition process. One that supports talk of movement using sensors on the MYO Armband tool. This paper explains how to use raw data generated from the MYO Armband sensor and extract integration so that it can be used to facilitate complete hand, arm and combination movements in the SIBI sign language dictionary. MYO armband uses five sensors: accelerometer, gyroscope, orientation, euler-orientation and EMG. Each sensor produces data that is different in scale and size. This requires a process to make the data uniform. This study uses the min-max method to normalize any data on the MYO Armband sensor and the Moment Invariant method to extract the vector features of hand movements. Testing is done using sign language Movement statistics both dynamic signals. Testing is done using cross validation.