Discriminant Analysis Based EMG Pattern Recognition for Hand Function Rehabilitation

Electromyographic (EMG) signal is playing an important role on hand function training as a neuromuscular rehabilitation tool. Various pattern recognition algorithms (PRAs) have been compared and evaluated in previous research, and Linear Discriminant Analysis (LDA) showed the higher offline accuracy for motion classification. However, it is rarely of comparison for different types of Discriminant Analysis (DA), and the surface electrodes are common methods for signal acquisition. This paper proposes to evaluate the offline performance of LDA and other types of DA, and using Myo armband for recording signals. The offline data was acquired by Myo armband, processing recognizing the data in BioPatRec, an open source platform for motion classification and hand prosthetics control. From the results of average offline accuracy, training time, and testing time of the five types, LDA and Quadratic Discriminant Analysis (QDA) have the better performance than others, and LDA is the fastest algorithm with simple computing.