Improved Prosthetic Control Based on Myoelectric Pattern Recognition via Wavelet-Based De-Noising
Journal article, 2018
Real-time inference of human motor volition has great potential for the intuitive control of robotic devices. Toward this end, myoelectric pattern recognition (MPR) has shown promise in the control of prosthetic limbs. Interfering noise and susceptibility to motion artifacts have hindered the use of MPR outside controlled environments, and thus represent an obstacle for clinical use. Advanced signal processing techniques have been previously proposed to improve the robustness of MPR systems. However, the investigation of such techniques have been limited to offline implementations with long time windows, which makes real-time use unattainable. In this work, we present a novel algorithm using discrete and stationary wavelet transforms for MPR that can be executed in real-time. Our wavelet-based de-noising algorithm outperformed conventional band-pass filtering (up to 100 Hz) and improved real-time MPR in the presence of motion artifacts, as measured by the motion test. Improved signal-to-noise ratio was found not to be crucial in offline MPR, as machine learning algorithms can integrate high but consistent noise as part of the signal. However, varying interference is expected to occur in real life where signal processing algorithms, as the one introduced in this paper, would potentially have a positive impact. Furthermore implementation of these algorithms in a prosthetic embedded system is required to validate their feasibility and usability during activities of the daily living.
myoelectric pattern recognition
Artificial neural networks (ANN)
discrete wavelet transforms