Abstract

Systems used for pattern recognition are usually divided into 4 stages: signal acquisition, preprocessing, feature extraction and classification. However, the use of algorithms in these last 3 stages is not justified and researchers use them without criteria other than the result achieved at the end of the process. In this paper we propose a new methodology and show its particular application to the recognition of five hand gestures based on 8 channels of Electromyography using the Myo armband device placed on the forearm. If n features are extracted, they will form clusters of points in n-dimensional space and now the selection of the best preprocessing algorithms and the best features are based on maximizing the distance among clusters. On the other hand, both intrapersonal and interpersonal variability are treated to facilitate the understanding of the phenomenon. As a demonstration, it was applied to 12 people and the recognition accuracy was 97%.