Date of Award
Spring 2023
Document Type
Thesis
Terms of Use
© 2023 Youssef Kharrat. This work is freely available courtesy of the author. It may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. For all other uses, please contact the copyright holder.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Degree Name
Bachelor of Arts
Department
Engineering Department
First Advisor
Maggie Delano
Abstract
This project introduces a classification model whose purpose is the distinction between the two possible movements done by the bicep; namely flexion and rotation. Surface EMG data was collected using the MIKROE-2621. It was later sampled from Analog to Digital by a Nucleo 401-RE board at 200 Hz. To generate the dataset, I connected the apparatus to my arm and performed the movements a total number of 300 times. This data was later analyzed and a choice of features were made that eventually was fed into MATLAB Classification Learner. A Support Vector Machine performed best and was able to classify the movements at a 94.3 % accuracy rate. This report shows the different techniques used to analyze the data and how I developed an understanding of the principal components that govern this classification using statistical methods.
Recommended Citation
Kharrat, Youssef , '23, "Model for movement classification of the bicep" (2023). Senior Theses, Projects, and Awards. 288.
https://works.swarthmore.edu/theses/288