title: Hand movements classification for myoelectric control system using adaptive resonance theory.
abstract: This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins' multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09%) but also a significantly improved computation time.
authors.affiliation: Control System and Signal Processing Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia. ee.hessam.jahani@gmail.com.
authors.forename: H
authors.initials: H
authors.lastname: Jahani Fariman
authors.affiliation: Control System and Signal Processing Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia.
authors.forename: Siti A
authors.initials: SA
authors.lastname: Ahmad
authors.affiliation: Control System and Signal Processing Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia.
authors.forename: M
authors.initials: M
authors.lastname: Hamiruce Marhaban
authors.affiliation: Control System and Signal Processing Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia.
authors.forename: M
authors.initials: M
authors.lastname: Alijan Ghasab
authors.affiliation: Electronics & Computer Science, University of Southampton, SO17 1BJ, Highfield, UK.
authors.forename: Paul H
authors.initials: PH
authors.lastname: Chappell
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D016000:Cluster Analysis; D019992:Databases as Topic; D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods*; D006225:Hand / Q000502:physiology*; D006801:Humans; D009068:Movement; D018482:Muscle, Skeletal / Q000033:anatomy & histology / Q000502:physiology; D055815:Young Adult
keywords: Adaptive resonance theory; EMG; Neural network; Pattern recognition; Prosthetic hand