This week, we ran and recorded several sets of data that were used to train and test our machine learning models. Our preliminary classification result appears to be accurate so far with only around a 2% detection error. This is because our data is purposely timed and collected and only a few general features were picked up (eg. max, min, etc.). We expect that as we move forward, there will be more challenges in classifying data that are not just binary but other types, including left vs. right blink, double blinks, and triple blinks. There is some noise within our window of data as well so we are trying to find a way to smooth that part out for a better fitting of our ML classification. Another thing that we are working on is to find out if there are other distinct artifacts apart from natural eye blinks. If there are, we will need to clear it out before processing the data. We also changed our design plans, specifically the frontend portion, to be on Python rather than Flutter. We are all more comfortable with this platform and will prototype all our features here. Lastly, we received our two EMG kits and will be playing around with that this coming week. We hope to be able to connect it up to the arduino and measure preliminary arm movement signals that will be useful control data. Overall as a team, we are on track.
Jonathan K., Wendy M., Jean U. – Carnegie Mellon ECE Capstone, Spring 2022