title: Confidence-based rejection for improved pattern recognition myoelectric control.
abstract: This study describes a novel myoelectric control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect only the rejection characteristics of the associated class. Furthermore, because the rejection stage is implemented using the outputs of the LDA, the active motion classification accuracy of the proposed system is shown to outperform that of the LDA for all values of rejection threshold. The proposed scheme was compared to a baseline LDA-based pattern recognition system using a real-time Fitts' law-based target acquisition task. The use of velocity-based myoelectric control using the rejection classifier is shown to obey Fitts' law, producing linear regression fittings with high coefficients of determination (R(2) > 0.943). Significantly higher (p < 0.001) throughput, path efficiency, and completion rates were observed with the rejection-capable system for both able-bodied and amputee subjects.
authors: Institute of Biomedical Engineering, University of NewBrunswick, Fredericton NBE3B 5A3, Canada. escheme@unb.ca
authors: Erik J
authors: EJ
authors: Scheme
authors: Bernard S
authors: BS
authors: Hudgins
authors: Kevin B
authors: KB
authors: Englehart
mesh_terms: D000328:Adult; D000465:Algorithms; D000674:Amputees; D001186:Artificial Limbs; D001499:Bayes Theorem; D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods*; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013647:Task Performance and Analysis; D014953:Wrist / Q000502:physiology