An LDA-based approach for real-time simultaneous classification of movements using surface electromyography

• Verfasst von: Antuvan, Chris Wilson [VerfasserIn]
• Verfasst von: Masia, Lorenzo [VerfasserIn]
• Titel: An LDA-based approach for real-time simultaneous classification of movements using surface electromyography
• Verf.angabe: Chris Wilson Antuvan, student member, IEEE , and Lorenzo Masia, member, IEEE
• E-Jahr: 2019
• Jahr: [March 2019]
• Umfang: 10 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 12.06.2019
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on neural systems and rehabilitation engineering
• Ort Quelle: New York, NY : IEEE, 1993
• Jahr Quelle: 2019
• Band/Heft Quelle: 27(2019), 3, Seite 552-561
• ISSN Quelle: 1558-0210
• DOI: doi:10.1109/TNSRE.2018.2873839
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Calibration
• Sach-SW: classification accuracy
• Sach-SW: classification strategy
• Sach-SW: combined motions
• Sach-SW: combined movements
• Sach-SW: Complexity theory
• Sach-SW: decoder
• Sach-SW: decoding
• Sach-SW: Decoding
• Sach-SW: decoding algorithm
• Sach-SW: decoding strategies
• Sach-SW: DOF task
• Sach-SW: electromyography
• Sach-SW: Electromyography
• Sach-SW: hand movements
• Sach-SW: human motor control strategy
• Sach-SW: human-machine interactions
• Sach-SW: individual motions
• Sach-SW: individual movements
• Sach-SW: learning (artificial intelligence)
• Sach-SW: linear discriminant analysis
• Sach-SW: low-dimensional representation
• Sach-SW: medical signal processing
• Sach-SW: Muscles
• Sach-SW: myoelectric signals
• Sach-SW: myoelectric-based movement control
• Sach-SW: real-time myoelectric control
• Sach-SW: real-time simultaneous classification
• Sach-SW: Real-time systems
• Sach-SW: signal classification
• Sach-SW: simultaneous decoding
• Sach-SW: simultaneous motion decoding
• Sach-SW: supervised decomposition algorithm
• Sach-SW: surface electromyography
• Sach-SW: Wrist
• Sach-SW: wrist movements
• K10plus-PPN: 1667287354

Abstract

Myoelectric-based decoding strategies offer significant advantages in the areas of human-machine interactions because they are intuitive and require less cognitive effort from the users. However, a general drawback in using machine learning techniques for classification is that the decoder is limited to predicting only one movement at any instant and hence restricted to performing the motion in a sequential manner, whereas human motor control strategy involves simultaneous actuation of multiple degrees of freedom (DOFs) and is considered to be a natural and efficient way of performing tasks. Simultaneous decoding in the context of myoelectric-based movement control is a challenge that is being addressed recently and is increasingly popular. In this paper, we propose a novel classification strategy capable of decoding both the individual and combined movements, by collecting data from only the individual motions. Additionally, we exploit low-dimensional representation of the myoelectric signals using a supervised decomposition algorithm called linear discriminant analysis, to simplify the complexity of control and reduce computational cost. The performance of the decoding algorithm is tested in an online context for the two DOFs task comprising the hand and wrist movements. Results indicate an overall classification accuracy of 88.02% for both the individual and combined motions.