Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods

• Published in: Biomedical signal processing and control Vol. 75; p. 103588
• Main Authors: Tepe, Cengiz, Erdim, Muhammed
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2022
• Subjects: Data processing and classification; Finger gestures; Gyroscopic; Myo armband; sEMG; Myo armband; sEMG; Data processing and classification; Finger gestures; Gyroscopic
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.103588

•Finger gestures are difficult to process due to overlapping layered structure of the arm muscles.•By examining the effects of gyroscope data, a new feature has been tried to be given to the literature.•EMG and gyroscope signals were acquired using Myo Armband with surface electrodes.•The data set used was shared in the supplementary documents section of the journal.•The highest performance features were determined using sequential forward feature selection.
Gestures of the human hand can be identified through processing of surface electromyography (sEMG) signals. The human hand can perform many gestures via manipulation of the fingers. With correct classification of finger gestures, the mobility of a prosthetic hand can be increased and provide greater functionally. In this study, reliable classification was obtained for sEMG finger data acquired from a Myo armband placed on the lower forearm. In order to improve classification, gyroscopic signals, not previously used in other studies, were investigated in the sEMG finger data. Data was acquired from ten normal subjects using the Myo armband to identify 6 finger gestures: thumb, index finger, middle finger, little finger, ring finger and rest. Participants repeated each gesture thirty times. sEMG signals were preprocessed to extract features. 17 features were used in the feature matrix. By using the sequential forward feature selection method, the highest performance feature set was determined. Support Vector Machine, K-Nearest Neighbor and multilayer artificial neural network were used as classification algorithm. The classification was made using the Classification Learner Application and Neural Network Pattern Recognition Tool in Matlab®. The best performance with the features extracted only from sEMG data was 94.40% using the Artificial Neural Networks (ANN) method. The best performance with the features extracted from both sEMG and gyroscopic data was 96.30% (p-value < 0.05)with the ANN method. It is seen that gyroscopic signals can increase classification performance.