This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered.

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This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643–60. DOI: /JRRD 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

This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643–60. DOI: /JRRD Study Aims – Describe issues and best practices in electromyogram (EMG) pattern recognition. – Identify major challenges in deploying robust control. – Advocate research directions. Relevance – Using EMG signals to control upper-limb prostheses offers autonomy of control via residual muscle contraction. – Pattern recognition to discriminate multiple degrees of freedom has shown great promise in research literature but not yet a clinically viable option.

This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643–60. DOI: /JRRD Simple one-muscle one-function approach to conventional control is naïve to complexities of EMG cross talk, muscle co-activation, and contribution of deep muscle. This has motivated use of pattern-recognition approach to myoelectric control. – By using multiple EMG sites, effective feature extraction, and multidimensional classifiers, one can achieve control of many more classes of motions.

This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643–60. DOI: /JRRD Pattern Recognition Stages of signal processing for EMG pattern recognition. All approaches to EMG pattern recognition go through these fundamental processing stages. Feature-extraction stage increases information density of EMG signals.

This article and any supplementary material should be cited as follows: Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. J Rehabil Res Dev. 2011;48(6):643–60. DOI: /JRRD Conclusions Best Practices – For slowly varying EMG patterns, time domain features offer suitable tradeoff in accuracy and computational complexity. – With appropriate feature set and sufficient channels, most modern classifiers will perform similarly. However, linear discriminant analysis is easy to implement and train. – Most meaningful assessment is function user derives from device. Major Challenges – Electrode shift, variation in force, variation in position of limb, and transient changes in EMG. Future Prospects – Wireless, implanted EMG sensors incorporate functional advantages of wire electrodes with minimal invasiveness.