Fusion of projected feature for classification of EMG patterns

In medical domain, proper set of information fusion is the vital requirement for quantitative interpretation. In this paper, we present a novel feature fusion technique based on two-fold feature projection (FP) for EMG classification. Canonical Correlation Analysis (CCA) transformation is performed on original feature space and wavelet transformation of original feature. Based on subspace learning technique, relevant features are extracted from unified domain independent spaces and fused via proposed fusion algorithms. To demonstrate the outperform of the adopted algorithm three class of EMG subject groups are considered from online and Guwahati Neurological Research Centre (GNRC), Ghy, India. Outcomes indicate that the adopted dimension reduction strategy are consistent not only in accuracy but also in other quality assessment parameters. The overall accuracy is 98.80% ±2.0%. It provides an efficient and powerful way of feature extraction for fusion to improve recognition rate. Hence, it promises to prove a better strategic tool for medical data analysis in healthcare institutions.