Noise Removal in EMG Signal Using Data Fusion Techniques

Abstract

One of the main challenges in processing the biomedical signals, such as ECG , EEG and EMG is noise removal as they are easily get affected by various noises arising from different environmental conditions. Filtering out the noise from EMG signal improves the accuracy and performance of signal processing systems. But in practice, it is very complicated to filter out noise from the desired EMG signals to obtain noise corrupted raw signal. This paper proposes a new data fusion techniques to reduce the effect of noise on electromyography signals, that are to be further processed to get the required information. The proposed method results in EMG signal enhancement when a corrupted emg signal with an additive white Gaussian noise is the only available information. The main idea is to utilize the kalman filter to remove the noise and enhance the performance of electromyography signals.