sEMG feature extraction using Generalized Discrete Orthonormal Stockwell Transform and Modified Multi-Dimensional Scaling

This paper proposes a method based on a generalized version of the Discrete Orthonormal Stockwell Transform (GDOST) with Gaussian window to extract features from surface electromyography (sEMG) signals in order to identify hand's movements. The features space derived from the GDOST is then reduced by applying a modified Multi-Dimensional Scaling (MDS) method. The proposed modification on MDS consists in using a translation in kernel building instead of the direct distance calculation. The results are compared with another study applied on the same dataset where usual DOST and MDS are applied. We achieved significant improvements in classification accuracy, attaining 97.56% for 17 hand movements.