Robust Pattern Recognition Myoelectric Training for Improved Online Control within a 3D Virtual Environment

It has been shown that maintaining a neutral arm position during collection of pattern recognition training data for myoelectric prosthesis control results in high offline classification accuracies; however, that precision does not translate to real-time applications, when the arm is used in different positions. Previous studies have shown that collecting training data with the arm in a variety of positions can improve pattern recognition control systems. In this work, we extended these findings to real-time myoelectric control in an immersive testing environment using virtual reality. We show that collecting training data for a pattern recognition algorithm under dynamic conditions, where the user moves their arm, significantly improves control efficiency and achievement of testing metrics.