title: Multifunction myoelectric control using multi-dimensional dynamic time warping.
abstract: Myoelectric control can be used for a variety of applications including powered protheses and different human computer interface systems. The aim of this study is to investigate the formulation of myoelectric control as a multi-class distance-based classification of multidimensional sequences. More specifically, we investigate (1) estimation of multi-muscle activation sequences from multi-channel electromyographic signals in an online manner, and (2) classification using a distance metric based on multi-dimensional dynamic time warping. Subject-specific results across 5 subjects executing 10 different hand movements showed an accuracy of 95% using offline extracted trajectories and an accuracy of 84% using online extracted trajectories.
authors.forename: Meena
authors.initials: M
authors.lastname: AbdelMaseeh
authors.lastname: Tsu-Wei Chen
authors.forename: Daniel
authors.initials: D
authors.lastname: Stashuk
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D006225:Hand / Q000502:physiology; D006801:Humans; D009068:Movement; D009862:Online Systems; D013997:Time Factors; D014584:User-Computer Interface