Enhancement of Upper Limb Movement Classification based on Wiener Filtering Technique

Electromyogram pattern recognition (EMG-PR) is considered a potential method for upper limb prosthesis control. In principle, the feature extraction technique has been ranked the most influential factor that affect the EMG-PR method's performance. Despite the progress made thus far, there are inevitable interferences that could not be handled by the usual signal filtering approaches that are applied to enhance the extracted features. To address this issue, this study proposed a technique based on Wiener filtering for the preprocessing of EMG signals towards increasing the classification performance of EMG-PR systems. The performance of the proposed approach was investigated with recordings of high-density surface EMG which was obtained from four transhumeral amputees who performed five classes of limb movements. Then, features of five time-domain were analyzed in terms of their decoding accuracy, sensitive, and F1-score, with and without the application of the proposed for linear discriminant analysis and support vector machine classifiers techniques. Experimental results showed that by applying the proposed technique to the different feature sets, significant improvements in classification accuracy, sensitivity, and F1-score were observed across all subjects and classifiers. The proposed method improved the average classification accuracy by an increase of approximately 6.24% compared with the conventional method, while an increment as high as 16.77% was recorded for individual classes of movements. The outcomes of this study indicate that Wiener filtering may potentially boost the performance of EMG-PR systems in practical applications.