title: Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications.
abstract: Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia. eng.mgd@gmail.com.
authors.forename: Maged S
authors.initials: MS
authors.lastname: Al-Quraishi
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia.
authors.forename: Asnor J
authors.initials: AJ
authors.lastname: Ishak
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia.
authors.forename: Siti A
authors.initials: SA
authors.lastname: Ahmad
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia.
authors.forename: Mohd K
authors.initials: MK
authors.lastname: Hasan
authors.affiliation: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riydh, 11543, Saudi Arabia.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Al-Qurishi
authors.affiliation: Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, 43400, Serdang, Malaysia.
authors.forename: Hossein
authors.initials: H
authors.lastname: Ghapanchizadeh
authors.affiliation: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riydh, 11543, Saudi Arabia.
authors.forename: Atif
authors.initials: A
authors.lastname: Alamri
mesh_terms: D000328:Adult; D000465:Algorithms; D000843:Ankle Joint / Q000502:physiology*; D001499:Bayes Theorem; D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D019736:Prostheses and Implants; D012371:Robotics / Q000379:methods; D012815:Signal Processing, Computer-Assisted
keywords: Ankle joint movements; EMG; Pattern recognition; Rehabilitation; Signal processing