Abstract
The application of electromyography (EMG) has shown great success in rehabilitation engineering. With the existing multiple-channel EMG recording system, the detection and classification of EMG pattern have become viable. The purpose of this study is to investigate the relation between sampling rate and EMG pattern recognition by using spectrogram. The features are extracted from spectrogram coefficients and the principal component analysis is applied for dimensionality reduction. In addition, the optimal Hanning window size is identified and selected before performance evaluation. For noise evaluation, the additive white Gaussian noise (AGWN) is added to the EMG signal at 30, 25, 20 dB SNR. The results illustrated that the 512 Hz sampling rate can maintain a small decrement of 0.76% accuracy compared to 1024 Hz. However, when the AGWN is added, the 256 and 512 Hz sampling rates showed a greater reduction in overall classification performance. For a lower SNR, the gaps in classification accuracy between 1024 Hz, 512 Hz and 256 Hz sampling rates are obviously presented. It signifies that reducing the sampling rate lower than 1024 Hz might not be a good choice since the noise and artifact have to be taken into consideration in a real system.
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