FPGA-based Implementation of Random Forest Classifier for sEMG Signal Classification

This paper proposes a method that combines a hardware description language and Random Forests theory, to develop a motion recognition embedded system. To validate this system, the Ninapro database DB2 was used as training and test data, resulting in an accuracy of 83.1% for the test base in movement prediction.