The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a novel approach to optimize pattern recognition system using genetic algorithm (GA) to identify the type of hand motion employing artificial neural networks (ANNs) with high performance and accuracy suited for practical implementations. To achieve this approach, electromyographic (EMG) signals were obtained from sixteen locations on the forearm of six subjects in ten hand motion...
Nowadays, it is common to identify some neuromuscular disorders from the myoelectric signals (MES). Often, these disorders are reflected in the basic components of the MES, the motor unit action potentials (MUAP). This work presents an approach for the decomposition of intramuscular MES in its essential MUAPs, through analysis (wavelet transform) and classification (neural networks) tools. Decomposition...
In this research, the artificial intelligent method based human motion pattern recognition for surface electromyographic (EMG) signal is proposed. As the EMG signal is a measurement of anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the technology of wavelet packet transformation, the...
An EMG-driven arm wrestling robot (AWR) is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The AWR arm have 2-DOF, integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera, is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). The surface...
In this paper, we develop a novel robotic arm wrestling system integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with real human on a table for entertainment. The designing scenario of the prototype model's hardware is performed. Elbow/wrist force sensors, as...
In this paper, the surface electromyographic (EMG) signals is acquired from the upper limb when the experimenter competes with the arm wrestling robot (AWR) which is integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with human on a table with pegs for entertainment...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
In this paper, an identification method of finger motions using the wavelet transform of multi-channel electromyography (EMG) signal is presented. The first step of this method is to analyze surface EMG signal detected from the subject's upper arm using the multi-resolution of wavelet transform, and extract features using the variance, maximum and mean absolute value of the wavelet coefficients. In...
Both independent component analysis (ICA) and principal component analysis (PCA) were used in this study to evaluate their effects in data reduction in the hand motion identification using surface electromyogram (SEMG) and stationary wavelet transformation. The results indicate that both methods increase the number of training epochs of the artificial neural network. The unsupervised fast ICA reduces...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.