title: Identification and classification of upper limb motions using PCA.
abstract: This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.
authors.affiliation: Electronics and Communication Engineering Department, Panjab University, Chandigarh, India.
authors.forename: Karan
authors.initials: K
authors.lastname: Veer
authors.affiliation: Electronics and Communication Engineering Department, Panjab University, Chandigarh, India.
authors.forename: Renu
authors.initials: R
authors.lastname: Vig
mesh_terms: D001132:Arm; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009038:Motion; D018482:Muscle, Skeletal / Q000502:physiology*; D025341:Principal Component Analysis; D034941:Upper Extremity / Q000502:physiology*
keywords: biceps muscles; biomedical signals; principal component analysis; signal acquisition; surface electromyogram