Gaussian Smoothing Filter for Improved EMG Signal Modeling

The goal of the research presented in this chapter is to improve the classification process of electromyography (EMG) signals that are contaminated with noise. If the existence of noise in EMG signals is not accounted for, it can degrade the performance of the classification task. Therefore, it is necessary to utilize an efficient filtering process to improve the classification of EMG signals. Guided by the need to filter the noise out of EMG signals, this chapter proposes to employ a Gaussian smoothing filter (GSF) that is simple in its implementation with an efficient filtering performance. The GSF, which is a Gaussian function, offers equal support in both frequency and time domains, allowing it to yield a performance compromise in removing the noise while preserving high frequency components of EMG signals. It is additionally shown that the use of GSF not only enhances the classification accuracy, but it also reduces the computational time needed in the training and testing of the classification process. To evaluate the performance of the GSF in EMG signals classification problem, two experiments are considered. The first experiment consists of classification of multiple hand gestures using EMG signals and the second experiment considers classifying phases of hand motion for a grasping task. An array of standard classification techniques are considered in both experiments and the use of GSF in filtering out noise is shown to enhance classification accuracy with remarkably reduced computational time for the considered classification techniques. This illustrates the feasibility of GSF in filtering EMG signals for classification tasks. To gain further insights into the GSF, its performance is compared with that of a median filter (MF), one of the well-known filtering techniques. By using the overall classification accuracy as an index of comparison, the GSF is shown to result in a superior classification accuracy, demonstrating its efficacy for EMG signals filtering process. Thus, employing GSF proves to provide enhancement in the classification accuracy and required computational efforts.