Abstract
This paper describes a novel post-processing algorithm for electromyographic (EMG) pattern classification, for use with myoelectric prosthetic hands. Amputees have difficulties controlling multiple degrees of freedom, but there is an increasing number of prosthetic hands with multiple degrees of freedom. Generally, an increasing number of classes decreases the classification accuracy. Artificial neural networks have been used for EMG pattern classification in previous studies. The proposed post-processing algorithm stores the temporal sequence of classifications from the EMG pattern classification algorithm, and runs a second classification based on the sequential patterns. We compared the accuracy of the output before and after the post-processing step. In our experiment, we set the training time of the EMG pattern classification algorithm to 1 s for each class, and used three channels of surface EMG signals. We selected 7 and 9 classes of EMG patterns, and recorded the output every 10-20ms. The classification accuracy improved by 11.5% with 7 classes, and 17.7% with 9 classes. The overall accuracy of the proposed system was 82.5% for 9 classes and 92.9% for 7 classes. With the adequately high classification accuracy and other features (small number of EMG channels and short training time), the proposed method is potentially suitable for practical use with prosthetic hands.
