Spectral and mathematical evaluation of electromyography signals for clinical use

The surface electromyography (SEMG) is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, LabviewⓇ-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to e...

[1]  Bruce C. Wheeler,et al.  EMG feature evaluation for movement control of upper extremity prostheses , 1995 .

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

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

[4]  Ravinder Agarwal,et al.  Wavelet denoising and evaluation of electromyogram signal using statistical algorithm , 2014 .

[5]  Roberto Merletti,et al.  Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art. , 2009, Clinical biomechanics.

[6]  Nils Östlund,et al.  Signal processing of the surface electromyogram to gain insight into neuromuscular physiology , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Marek Kurzynski,et al.  Multiclassifiers with competence function applied to the recognition of emg signals for the control of bio-prosthetic hand , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[8]  Suncheol Kwon,et al.  Real-Time Upper Limb Motion Estimation From Surface Electromyography and Joint Angular Velocities Using an Artificial Neural Network for Human–Machine Cooperation , 2011, IEEE Transactions on Information Technology in Biomedicine.

[9]  J. Rafiee,et al.  Feature extraction of forearm EMG signals for prosthetics , 2011, Expert Syst. Appl..

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

[11]  Gregory T. A. Kovacs,et al.  Optical Scanner for Immunoassays With Up-Converting Phosphorescent Labels , 2008, IEEE Transactions on Biomedical Engineering.

[12]  Guanglin Li,et al.  Modeling of Prosthetic Limb Rotation Control by Sensing Rotation of Residual Arm Bone , 2008, IEEE Transactions on Biomedical Engineering.

[13]  Amod Kumar,et al.  Processing and interpretation of surface electromyogram signal to design prosthetic device , 2016, Robotica.

[14]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[15]  G.S. Dhillon,et al.  Direct neural sensory feedback and control of a prosthetic arm , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  S Micera,et al.  Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.

[17]  Xu Zhang,et al.  Wavelet transform theory and its application in EMG signal processing , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[18]  C M Light,et al.  Intelligent multifunction myoelectric control of hand prostheses , 2002, Journal of medical engineering & technology.

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

[20]  Dinesh Kant Kumar,et al.  Computation of fractal features based on the fractal analysis of surface Electromyogram to estimate force of contraction of different muscles , 2014, Computer methods in biomechanics and biomedical engineering.

[21]  Panagiotis K. Artemiadis,et al.  An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.

[22]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[23]  Maria Konarska,et al.  Characteristics of power spectrum density function of EMG during muscle contraction below 30%MVC. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[24]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.