A Self-Selected Movement Classification Method for Forearm Via sEMG and Attitude Sensor

Abstract

<jats:title>Abstract</jats:title> <jats:p><jats:italic>Objective</jats:italic>: The use of surface electromyography (sEMG) to realize the recognition of the movement intention can realize the control of the artificial hand or the robot, and can help the rehabilitation training for hemiplegia or muscle weakness. However, the sEMG are weak and susceptible to external interference, so the current research focuses on identifying certain types of movements. But once the subjects are changed, the recognition accuracy will greatly reduce. This study proposes a classification method which the subject could choose optional movements of forearm.<jats:italic>Methods</jats:italic>: Two sEMG sensors were used, and a 9-axis attitude sensor was added to the wrist. 8 different subjects participated in the experiment, and everyone selected 5 movements. The sEMG sensors were attached to the extensor pollicis brevis and the extensor digitorum. The sEMG features were: Standard Deviation (SD), Power Spectrum Density (PSD); attitude sensor features were: angle and angular acceleration in three dimensional space, and integral of angular acceleration. The results were classified and identified using Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (DT) and Ensembles (En) algorithms. The results of using the sEMG, using the attitude sensor signals and combining the two were compared. Analysis of variance was conducted on the average accuracy. Features were reduced the dimension by the Principal Component Analysis (PCA), and the results of using PCA and not were compared. <jats:italic>Results</jats:italic>: The results showed that the combination of the two types of sensors could improve the recognition effect compared to the using sEMG sensor or the attitude sensor alone. The final recognition result was that KNN performed best, reaching 95.0%. The results of using PCA were more stable.<jats:italic>Conclusion</jats:italic>: The method could be used between different subjects, and the user could select the movements autonomously.<jats:italic>Significance</jats:italic>: This method can improve the adaptability of movement intention recognition based on sEMG, and has important significance for popularizing the use of the sEMG to control the manipulator or the prosthetic and the rehabilitation training.</jats:p>