This week we did our interim demo, we got our glove designed with all the parts mounted onto it via a mini bread-board. We have a through hole breadboard we intend on soldering our parts using to make the product lightweight. This week we will officially add the touch sensors onto the glove as we just completed the testing phase for them.
As for the software, Nia and Kat are working on the data collection script and making it run at the same time as the MicroPython on the Pico. This week we plan on doing data collection with the touch sensors and being able to train the model by the end of the week.
For testing, Katherine made a script that accepted the sensors values and printed their outputs. She calibrated the data collection for optimal accuracy for the flex sensors while bending and straightening the fingers. For our machine learning model, we are outputting the accuracy and loss calculations after training. We are shooting for 95% training accuracy. As of right now we have 3 data vectors per sign, however Katherine and Nia are still working on collecting more. Since Teadora is still adding the touch sensors to the glove, we are manually inputting the touch sensor values for now as they are binary (touched or not touched. The more data we add, the better our accuracy should be. Katherine is implementing a K-NN model and Nia a Neural Network model that we can evaluate both training accuracies on and whichever is better we will use for our final implementation.