While doing my research, I came across several articles about the use of machine learning models for interpreting emg signals.
Kok, C.L., Ho, C.K., Tan, F.K. and Koh, Y.Y., 2024. Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control. Applied Sciences, 14(13), p.5784.
The paper explores different machine learning techniques used for feature extraction and classification for EMG signals for intuitive prosthetic control. Researchers used already existing dataset: GRABMyo Dataset, a comprehensive collection of electromyographic (EMG) signals recorded during various hand gestures. The results of this study indicate that the model combining Linear Discriminant Analysis with the Support Vector Machine (LDA–SVM) exhibits superior accuracy in classifying EMG signals for prosthetic control, achieving 90.69% accuracy with a comprehensive dataset spanning three sessions.
First meeting after the winter submissions:
I was advised to look at the experiments from a different perspective, and try to interpret the paper in different ways, try with different technologies or datasets in order to formulate my idea.
I was also advised to do a secondary research in order to identify a user group I am going to aim my project at.
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