Myoelectric signal segmentation and classification using wavelets based neural networks

In this paper a method for Myoelectric signal (MES) segmentation and classification is proposed. The classical moving average technique augmented with Principal Components Analysis (PCA), and time-frequency analysis were used for segmentation. Multiresolution Wavelet Analysis (MRWA) was adopted as an effective feature extraction technique while Artificial Neural Networks (ANN) was used for MES classification. Results of classifying four elbow and wrist movements gave 94.9% sensitivity and 94.9% positive predictivity.