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This study is on the emotion recognition based on electromyography (EMG) signal. EMG signals from multiple subjects were collected when film clips were shown to them, next all of the data were de-noised and original features were extracted with wavelet transform method, then feature selection was done using the Tabu search (TS) algorithm combined with the Sequential Backward Selection (SBS), at last...
In this paper, a multi-channel electromyogram acquisition system using programmable system on chip (PSOC) microcontroller was used to obtain the surface of EMG signal. Two pairs of single-channel surface electrodes were used to measure and record the EMG signal on forearm muscles. Then, different levels of Daubechies Wavelet family were performed to analyze the EMG signal. Finally, features in terms...
The amplitude of the surface electromyogram (sEMG) is frequently used as the control input to myoelectric prostheses, as a measure of muscular effort and has also been investigated as an indicator of muscle force. To cope with the non stationary property of sEMG, features were extracted using wavelet packet transform. The wavelet packet transform provide an effective representation for multi class...
In this paper, a novel electromyographic (EMG) motion pattern classifier using wavelet packet transform (WPT) and Learning Vector Quantization (LVQ) Neural Networks is proposed. This motion pattern classifier can successfully identify wrist extension, wrist flexion, hand extension and hand grasp, by measuring the surface EMG signals through two electrodes mounted on forearm extensor carpi ulnaris...
In recent years, electromyogram signal (EMG) feature selection, based on wavelet transform, has received considerable attention. This study introduces a new multi- wavelet function for surface EMG (sEMG) signal intended for tasks that involve hand movement recognition. To create the new wavelet function, several types of well known mother wavelet were exploited and through their integration the proposed...
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...
Electromyography (EMG) became noisy in the collection and transmission. To eliminate the noise, a novel threshold value method based on the wavelet de-noise was proposed. Firstly, the obtained EMG signal was decomposed by the wavelet transform. Then, the decomposed wavelet coefficients were analysed by the weighted average of traditional soft-threshold and hard-threshold. Finally, the wavelet coefficients...
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...
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