title: Selective classification for improved robustness of myoelectric control under nonideal conditions.
abstract: Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.
authors.affiliation: Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada. escheme@unb.ca
authors.forename: Erik J
authors.initials: EJ
authors.lastname: Scheme
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Englehart
authors.forename: Bernard S
authors.initials: BS
authors.lastname: Hudgins
mesh_terms: D000465:Algorithms; D000674:Amputees / Q000534:rehabilitation*; D000704:Analysis of Variance; D001185:Artificial Intelligence; D001186:Artificial Limbs; D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods*; D018737:Hand Strength; D006801:Humans; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D012815:Signal Processing, Computer-Assisted; D014953:Wrist / Q000502:physiology