The article shows that the surface electromyographic signal (sEMG) is a complex signal which regi... more The article shows that the surface electromyographic signal (sEMG) is a complex signal which registration is accompanied by various noises and interferences significantly complicating the analysis. The methods and parame- ters of the wavelet filtering of EMG signal in order to remove the noise and interference from out the recorded in real conditions pEMG the signal presented.
Experimental and mathematical modeling of the effectiveness of the proposed method of filtering noise and in- terference from biceps pEMG signal recorded while flexing of the elbow was shown, allowing to improve the per- formance of mechatronic systems using electromyographic signals for control.
The use of bioelectrical signals to control prosthetic limbs and other mechatronic devices, that ... more The use of bioelectrical signals to control prosthetic limbs and other mechatronic devices, that emulate the movement of a human hand, requires the solution of many tasks including the task of creating an adequate kinematic and dynamic models of the controlled object. The purpose of this study is to develop dynamic models of elbow motion in flexion and extension, adequate to the real biological system. The muscle contraction mechanism and the mathematical model of the EMG signal were considered. Mass - inertial characteristics (MIC) of the upper limbs were determined by method of geometric modeling together with the use of the MIC regression equation depending on the weight and height of person. The dynamic model was synthesized on the basis of the Lagrange equations, manipulating robots based on linearbiomechanical Hill muscle model. Simulation was performed among SimMechanics of Matlab. Analysis of the results showed that the adequacy of the mathematical model depends on the muscles characteristics such as viscosity and elasticity. The developed model can be used to design bioelectrical control systems of mechatronic devices.
The research objective is to study elbow flexion kinematic parameters using the artificial neural... more The research objective is to study elbow flexion kinematic parameters using the artificial neural networks (ANN). Parameters of the surface electromyogram (sEMG) are used as ANN inputs. The ANN output is kinematic parameters of motion: direction, angular displacement, and angular velocity. The study has involved DSTU students and staff (11 people without pathologies of the musculoskeletal system). The sEMG signals taken from the biceps of each trial subject during no-load elbow bending are registered. During the experiment, shoulder and elbow joints are fixed by the passive exoskeleton. The feature vector for the neural network is formed using methods of the spectral and statistical analysis. The statistical analysis in the time domain includes the determination of the following parameters: dispersion of sEMG amplitude values, arithmetic mean value and mean-square value of sEMG absolute amplitudes, sEMG signal zero crossing rates. In the frequency domain, sEMG signal spectral analysis is performed by Fast Fourier Transform method. The power spectrum and the mean frequency of the power spectrum are determined. Best results of determining the kinematic parameters are obtained when using the mean frequency of the power spectrum and the total integrated sEMG signal power as inputs to the ANN. The ANN is trained by the method of the direct signal propagation and the back propagation of error. The results obtained can be used in the development of the bioelectric control systems for the mechatronic devices.
Different methods and parameters of the wavelet transform of surface electromyographical signal (... more Different methods and parameters of the wavelet transform of surface electromyographical signal (sEMG) to remove noise and interference were investigated. Diagram of the complex values sEMG signal with current noise and interference from various sources was composed. The noise and interference induced on surface electrodes from different sources being. registered by the developed equipment. The amplitude-frequency analysis of the noise and interference was performed in LabVIEW, and the optimal method of wavelet transform for filtering sEMG signal being found to improve the quality of systems using sEMG signals for diagnosis or control of mechatronic devices.
The aim of this study was investigate noises and interferences which disturb the surface electr... more The aim of this study was investigate noises and interferences which disturb the surface electromyography signal (sEMG). It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. The internal noise are caused by the electrodes, EMG signals of other muscles; noise associated with the functioning of other organs such as the heart or stomach. The external noses are due to electrical environment the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electro motors. The block diagram of the noise sources was developed and with accordance with the diagram EMG signal was simulated. Denosing of simulated EMG signal was fulfilled by different wavelets and compare with digital filtering. The smallest error was observed in the case when using wavelet db4 of level 6.
The article shows that the surface electromyographic signal (sEMG) is a complex signal which regi... more The article shows that the surface electromyographic signal (sEMG) is a complex signal which registration is accompanied by various noises and interferences significantly complicating the analysis. The methods and parame- ters of the wavelet filtering of EMG signal in order to remove the noise and interference from out the recorded in real conditions pEMG the signal presented.
Experimental and mathematical modeling of the effectiveness of the proposed method of filtering noise and in- terference from biceps pEMG signal recorded while flexing of the elbow was shown, allowing to improve the per- formance of mechatronic systems using electromyographic signals for control.
The use of bioelectrical signals to control prosthetic limbs and other mechatronic devices, that ... more The use of bioelectrical signals to control prosthetic limbs and other mechatronic devices, that emulate the movement of a human hand, requires the solution of many tasks including the task of creating an adequate kinematic and dynamic models of the controlled object. The purpose of this study is to develop dynamic models of elbow motion in flexion and extension, adequate to the real biological system. The muscle contraction mechanism and the mathematical model of the EMG signal were considered. Mass - inertial characteristics (MIC) of the upper limbs were determined by method of geometric modeling together with the use of the MIC regression equation depending on the weight and height of person. The dynamic model was synthesized on the basis of the Lagrange equations, manipulating robots based on linearbiomechanical Hill muscle model. Simulation was performed among SimMechanics of Matlab. Analysis of the results showed that the adequacy of the mathematical model depends on the muscles characteristics such as viscosity and elasticity. The developed model can be used to design bioelectrical control systems of mechatronic devices.
The research objective is to study elbow flexion kinematic parameters using the artificial neural... more The research objective is to study elbow flexion kinematic parameters using the artificial neural networks (ANN). Parameters of the surface electromyogram (sEMG) are used as ANN inputs. The ANN output is kinematic parameters of motion: direction, angular displacement, and angular velocity. The study has involved DSTU students and staff (11 people without pathologies of the musculoskeletal system). The sEMG signals taken from the biceps of each trial subject during no-load elbow bending are registered. During the experiment, shoulder and elbow joints are fixed by the passive exoskeleton. The feature vector for the neural network is formed using methods of the spectral and statistical analysis. The statistical analysis in the time domain includes the determination of the following parameters: dispersion of sEMG amplitude values, arithmetic mean value and mean-square value of sEMG absolute amplitudes, sEMG signal zero crossing rates. In the frequency domain, sEMG signal spectral analysis is performed by Fast Fourier Transform method. The power spectrum and the mean frequency of the power spectrum are determined. Best results of determining the kinematic parameters are obtained when using the mean frequency of the power spectrum and the total integrated sEMG signal power as inputs to the ANN. The ANN is trained by the method of the direct signal propagation and the back propagation of error. The results obtained can be used in the development of the bioelectric control systems for the mechatronic devices.
Different methods and parameters of the wavelet transform of surface electromyographical signal (... more Different methods and parameters of the wavelet transform of surface electromyographical signal (sEMG) to remove noise and interference were investigated. Diagram of the complex values sEMG signal with current noise and interference from various sources was composed. The noise and interference induced on surface electrodes from different sources being. registered by the developed equipment. The amplitude-frequency analysis of the noise and interference was performed in LabVIEW, and the optimal method of wavelet transform for filtering sEMG signal being found to improve the quality of systems using sEMG signals for diagnosis or control of mechatronic devices.
The aim of this study was investigate noises and interferences which disturb the surface electr... more The aim of this study was investigate noises and interferences which disturb the surface electromyography signal (sEMG). It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. The internal noise are caused by the electrodes, EMG signals of other muscles; noise associated with the functioning of other organs such as the heart or stomach. The external noses are due to electrical environment the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electro motors. The block diagram of the noise sources was developed and with accordance with the diagram EMG signal was simulated. Denosing of simulated EMG signal was fulfilled by different wavelets and compare with digital filtering. The smallest error was observed in the case when using wavelet db4 of level 6.
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Papers by Anatoli Litvin
Experimental and mathematical modeling of the effectiveness of the proposed method of filtering noise and in- terference from biceps pEMG signal recorded while flexing of the elbow was shown, allowing to improve the per- formance of mechatronic systems using electromyographic signals for control.
Experimental and mathematical modeling of the effectiveness of the proposed method of filtering noise and in- terference from biceps pEMG signal recorded while flexing of the elbow was shown, allowing to improve the per- formance of mechatronic systems using electromyographic signals for control.