Real-Time Classification of EMG Myo Armband Data Using Support Vector Machine

• Published in: Ingénierie et recherche biomédicale 2022-08, Vol.43 (4), p.300-308
• Main Authors: Tepe, C., Demir, M.C.
• Format: Article
• Language: English
• Subjects: EMG; Hand gestures; Myo armband; Real-time classification; Support vector machine
• ISSN: 1959-0318
• DOI: 10.1016/j.irbm.2022.06.001

This study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results. EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition. 14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set. The greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures. •Reliable real-time classification of EMG data achieved using SVM algorithm and one feature.•Performance using individual and generalized EMG data compared.•Training on individual data determined to give significant better accuracy•Participants observed to train to repeat activation of relevant muscles.