IDENTIFICATION OF THE ELBOW MOTION KINEMATIC PARAMETERS BY MEANS OF ARTIFICIAL NEURAL NETWORKS TECHNOLOGY
Rubrics: MECHANICS
Authors:
Type:
Article
Pages:
from 138
to 143
Status:
Published
Received:
26.03.2015
Accepted:
31.03.2015
Published:
31.03.2015
Language:
Russian
Keywords:
passive exoskeleton, elbow motion parameters, electromyographic signal, statistical analysis, spectral analysis, artificial neural network, Matlab, Simulink
Abstract (English):
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 in-tegrated sEMG signal power as inputs to the ANN. The ANN is trained by the method of the direct signal propa-gation and the back propagation of error. The results obtained can be used in the development of the bioelec-tric control systems for the mechatronic devices.
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 in-tegrated sEMG signal power as inputs to the ANN. The ANN is trained by the method of the direct signal propa-gation and the back propagation of error. The results obtained can be used in the development of the bioelec-tric control systems for the mechatronic devices.
Keywords:
passive exoskeleton, elbow motion parameters, electromyographic signal, statistical analysis, spectral analysis, artificial neural network, Matlab, Simulink
passive exoskeleton, elbow motion parameters, electromyographic signal, statistical analysis, spectral analysis, artificial neural network, Matlab, Simulink
