Quantification and solutions of arm movements effect on sEMG pattern recognition
Arm movements, especially the dynamic change of arm position, have adverse effect on surface electromyography pattern recognition of wrist or hand motions, which results in very high classification errors. We adopted three metrics to quantify the changes of characteristics in the feature space due to arm movements. Arm movements caused the deviation of class centroid and made the intra-class dispersion greater. A robust training scheme was used to eliminate this effect and a novel classifier, conditional Gaussian mixture model, was proposed to improve the classification performance under this training scheme.
arm movements pattern recognition robustness surface electromyography (semg) transradial prostheses