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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.

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

1 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:10.1682/JRRD.2010.09.0177 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

2 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:10.1682/JRRD.2010.09.0177 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.

3 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:10.1682/JRRD.2010.09.0177 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.

4 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:10.1682/JRRD.2010.09.0177 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.

5 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:10.1682/JRRD.2010.09.0177 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.

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