GADF/GASF-HOG:feature extraction methods for hand movement classification from surface electromyography.

Objective: Human intention gesture recognition is widely used in hand rehabilitation, artificial limb control, teleoperation, human-computer interaction and other fields. It has great application value, however, how to extract human intention gesture accurately has been a research hotspot. Approach: Inspired by the image processing technology of machine vision, the surface electromyographic (sEMG) signal was selected as the source signal of motion intention in this work, and the original sEMG signal was converted into Gramian Angular Summation/Difference Field (GASF/GADF) image. Then, Histogram of Oriented Gradient (HOG) features of the corresponding GADF and GASF image were extracted. The extracted features are named as GASF-HOG and GADF-HOG. The Bagging method was used to map the features to six common gestures to realize the classification of intention gestures. Ten volunteers participated in the experiment, and the experimental data were used to verify the proposed method. Main results: The experimental results showed that the average accuracies of the proposed methods (GADF-HOG with Bagging, GASF-HOG with Bagging) were as follow: GADF-HOG with Bagging was with 95.73 ± 1.90%, and GASF-HOG with Bagging was with 93.63 ± 1.54%. Significance: The method proposed in this paper is inspired by image processing technology of machine vision, which provides a new idea about the human intention gesture recognition by combining the interdisciplinary knowledge.