today was one of two lock in days. i had to get some data, train a model, and do some real time classification. i started with just two states: rest or clench of fist. that worked well with an LDA model (100% accuracy) and real time worked well too. next i did a larger set with 6 distinct grips. this took some time to collect and clean up and visualize, and i started trying to train models on it. LDAs had too low accuracy and SVMs were overfitting the data; so while they had a 95% accuracy on paper, real-time classification was horrible. I think i have found the main issue after some serious debugging. the issue is that for some odd reason, the rest baseline drifts over the collection period. i have no idea why but since its drifting the entire LDA is getting thrown off at new data. and since teh SVM data was shuffled it looked fine on paper but real time just didnt work. i need to next adjust for that drift, create a video with the footage i created, update the github, polish the project a ltitle more and hopefully get ts submitted tommorow.