643 JRRD JRRD Volume 48, Number 6, 2011 Pages 643–660 Journal of Rehabilitation Research & Development Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use Erik Scheme, MSc, PEng; Kevin Englehart, PhD, PEng * Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada 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 control- ling 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 op tion. 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. Key words: amputee, electromyogram, EMG, linear discrimi- nant analysis, myo electric control, patt ern recognition, pros- thetics, rehabilitation, signal processing, upper limb. INTRODUCTION The use of the ele ctromyogram (EMG) a s a control source for po wered upper-limb prostheses has received considerable attention, because the idea of restoring func- tion by bridging natural neural pathways is a compelling pursuit. The most straight forward and widely used approach to estimating mot or intent is by estimating the intensity of the EMG from electrodes placed on the skin surface, usually placed directly above the remaining mus- cles that provide the strongest and most stable signal [1]. While basic function can be established in this manner, the corresponding cont rol is seldom intuitive and does not permit effective control of multiple joints in a pros - thetic limb. Although myoelectric prostheses have found an important place a s a clinical option in upper -limb prosthetics, the limited dexterity of control is often cited as the primary reason for rather low acceptance of these devices [2]. Conventional myoelectric control schemes use an amplitude measure at each electrode site (suc h as the root-mean-square or mean absolute value of the EMG) to quantify the intensity of contraction in the underlying muscles. Control is achieved by mapping this activity to the required prosthetic functio n; therefore, it is desirable that these muscles be functionally related to the functions that are to be resto red. If ph ysiologically appropriate muscles are available to res tore lost function, the EMG can be used intuitively, such as when a person with trans- humeral amputation controls a prosthetic elbow by using the residual biceps and triceps. In the absence of physio- logically appropriate musculature, substitutions must be used, such as using the wrist flexors/extensors to control a hand. If m ore than one device is to b e used, mode Abbreviations: ACE = Acquisition and Control Environment, AER = active error rate, AR = autoregressive, EMG = elec- tromyogram, IMES = Implantable MyoElectric Sensor, LDA = linear discriminant analysis, TD = time-domain, TDAR = time- domain/autoregressive, TER = total error rate, TMR = targeted muscle reinnervation. * Address all correspondence to Kevin Englehart, PhD, PEng; Institute of Biomedical Engineering, University of New Brunswick, 25 Dineen Drive, Fredericton, New Bruns- wick E3B5A3, Canada; 506-458-7020; fax: 506-453-4827. Email: kengleha@unb.ca DOI:10.1682/JRRD.2010.09.0177