title: Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.
abstract: Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.
authors.affiliation: Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada.
authors.forename: Erik
authors.initials: E
authors.lastname: Scheme
authors.forename: Kevin
authors.initials: K
authors.lastname: Englehart
mesh_terms: D000671:Amputation, Surgical / Q000534:rehabilitation*; D001132:Arm; D001186:Artificial Limbs; D005544:Forecasting; D006801:Humans; D058765:Neurofeedback; D010363:Pattern Recognition, Automated