Physics

Pattern recognition (psychology)

Meteorology

Artificial intelligence

Control (management)

Virtual reality

Training (meteorology)

Computer science

Training set

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.

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