System training and assessment in simultaneous proportional myoelectric prosthesis control. 2014

Anders L Fougner, and Oyvind Stavdahl, and Peter J Kyberd
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway. Anders.Fougner@itk.ntnu.no.

BACKGROUND Pattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme. METHODS In this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system's performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching. RESULTS The practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures. CONCLUSIONS This is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.

UI MeSH Term Description Entries
D008297 Male Males
D010363 Pattern Recognition, Automated In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed) Automated Pattern Recognition,Pattern Recognition System,Pattern Recognition Systems
D011474 Prosthesis Design The plan and delineation of prostheses in general or a specific prosthesis. Design, Prosthesis,Designs, Prosthesis,Prosthesis Designs
D004576 Electromyography Recording of the changes in electric potential of muscle by means of surface or needle electrodes. Electromyogram,Surface Electromyography,Electromyograms,Electromyographies,Electromyographies, Surface,Electromyography, Surface,Surface Electromyographies
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D001132 Arm The superior part of the upper extremity between the SHOULDER and the ELBOW. Brachium,Upper Arm,Arm, Upper,Arms,Arms, Upper,Brachiums,Upper Arms
D001186 Artificial Limbs Prosthetic replacements for arms, legs, and parts thereof. Arm Prosthesis,Arm, Artificial,Artificial Arm,Artificial Leg,Extremities, Artificial,Leg Prosthesis,Leg, Artificial,Limb Prosthesis,Limbs, Artificial,Arm Prostheses,Arms, Artificial,Artificial Arms,Artificial Extremities,Artificial Extremity,Artificial Legs,Artificial Limb,Extremity, Artificial,Leg Prostheses,Legs, Artificial,Limb Prostheses,Limb, Artificial,Prostheses, Arm,Prostheses, Leg,Prostheses, Limb,Prosthesis, Arm,Prosthesis, Leg,Prosthesis, Limb

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