Flexible Approach for Classifying EMG Signals for Rehabilitation Applications

Generally, the system used for recording and analysis of surface electromyography (sEMG) signals consists of an acquisition card (data), a preamplifier, and a software code for signal acquisition and signal conditioning at different stages (e.g., utilization of wavelet transform) before feeding to statistical evaluation. In our study, basically, two independent muscle locations ( m.m. biceps and triceps of the human upper limb) were selected for the recording of data with multiple motion activities; analysis of the recorded data was carried out based on the extracted parameters. Further, a computational tool of analysis of variance (ANOVA) algorithm and wavelet decomposition db2 were implemented and consequently used for identifying the best mechanism for dual-channel muscle positions in the course of independent arm movements. The respective procedures are described; the results obtained are analyzed from the aspect of the possibility of their use for the control of a robot arm and upper limb prostheses.

[1]  Yinfeng Fang,et al.  Interface Prostheses With Classifier-Feedback-Based User Training , 2017, IEEE Transactions on Biomedical Engineering.

[2]  Fumitoshi Matsuno,et al.  Hand and Wrist Movement Control of Myoelectric Prosthesis Based on Synergy , 2015, IEEE Transactions on Human-Machine Systems.

[3]  G. D. P. M. DipTP.  Introduction to Surface Electromyography , 1998 .

[4]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  K. Englehart,et al.  Electromyogram Whitening for Improved Classification Accuracy in Upper Limb Prosthesis Control , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Erik J. Scheme,et al.  Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions , 2011, IEEE Transactions on Biomedical Engineering.

[7]  Karan Veer,et al.  A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier , 2015 .

[8]  A. Phinyomark,et al.  An optimal wavelet function based on wavelet denoising for multifunction myoelectric control , 2009, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[9]  Amod Kumar,et al.  Design of a below elbow myoelectric arm with proportional grip force , 2012 .

[10]  Tanu Sharma,et al.  EMG classification using wavelet functions to determine muscle contraction , 2016, Journal of medical engineering & technology.

[11]  S. Arjunan,et al.  Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram of Tibialis Anterior , 2016, BioMed research international.

[12]  Toshio Tsuji,et al.  A Hybrid Motion Classification Approach for EMG-Based Human–Robot Interfaces Using Bayesian and Neural Networks , 2009, IEEE Transactions on Robotics.

[13]  Todd A. Kuiken,et al.  A Decision-Based Velocity Ramp for Minimizing the Effect of Misclassifications During Real-Time Pattern Recognition Control , 2011, IEEE Transactions on Biomedical Engineering.

[14]  Marek Kurzynski,et al.  Human-machine interface in bioprosthesis control using EMG signal classification , 2010, Expert Syst. J. Knowl. Eng..

[15]  Yoshiaki Hayashi,et al.  An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Karan Veer.  INTERPRETATION OF SURFACE ELECTROMYOGRAMS TO CHARACTERIZE ARM MOVEMENT , 2014 .

[17]  A. Phinyomark,et al.  Optimal Wavelet Functions in Wavelet Denoising for Multifunction Myoelectric Control , 2009, ECTI Transactions on Electrical Engineering, Electronics, and Communications.

[18]  S. A. Ahmad,et al.  Surface Electromyography Feature Extraction Based on Wavelet Transform , 2012 .

[19]  Sang Wook Lee,et al.  Subject-Specific Myoelectric Pattern Classification of Functional Hand Movements for Stroke Survivors , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Karan Veer.  Experimental Study and Characterization of SEMG Signals for Upper Limbs , 2015 .

[21]  Jhonatan Camacho Navarro,et al.  EMG-BASED SYSTEM FOR BASIC HAND MOVEMENT RECOGNITION , 2012 .

[22]  Levi J. Hargrove,et al.  Classification of Simultaneous Movements Using Surface EMG Pattern Recognition , 2013, IEEE Transactions on Biomedical Engineering.

[23]  K. Veer,et al.  Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals , 2015 .