Improvements on EMG-based handwriting recognition with DTW algorithm

Previous works have shown that Dynamic Time Warping (DTW) algorithm is a proper method of feature extraction for electromyography (EMG)-based handwriting recognition. In this paper, several modifications are proposed to improve the classification process and enhance recognition accuracy. A two-phase template making approach has been introduced to generate templates with more salient features, and modified Mahalanobis Distance (mMD) approach is used to replace Euclidean Distance (ED) in order to minimize the interclass variance. To validate the effectiveness of such modifications, experiments were conducted, in which four subjects wrote lowercase letters at a normal speed and four-channel EMG signals from forearms were recorded. Results of offline analysis show that the improvements increased the average recognition accuracy by 9.20%.