In this project, I utilized an MYO band to record 8 EMG signals. These signals were processed using an LDA classification algorithm, and the results were used to control a prosthetic hand with five distinct movements.
Initially, I received a basic framework for the MYO band system and my responsibility was to implement the live classification machine learning algorithm.
The system incorporates a Butterworth bandpass and bandpass filter to enhance the signal quality.
For the LDA model, I utilized the Sklearn library, which provided the necessary tools and functionalities.
Once the system was properly connected to the MAC address of the MYO band, it operated smoothly. During the training process, the accuracy of the confusion matrix was determined by the input data.
Overall, the project was successful in utilizing the MYO band and implementing the live classification machine learning algorithm, resulting in effective control of the prosthetic hand.
