At the start of the week, I handled the training of the model with the dataset that we collected from the previous week. I was able to train a simple NN architecture to a high level of test accuracy. (See below for training visualization). Afterward, I was able to go back and test real-time performance. I didn’t test to a rigorous degree but I was able to replicate a high degree of performance on the glove as well. I was also able to quickly check the speed of the ML classification step. This step identified that the inference step takes an infinitesimal amount of time. This allows me to meet my design requirements of an accuracy above 90% and inference time of less than 25ms. I also took some time to add an additional classification heuristic on top of the ML model. This was implemented using an additional wrapper on the ML model, which checks for repeated occurrences of the same letter before firm prediction. I also added speaker capabilities from the pyttsx3 library in Python. I also spent a significant portion of the week debugging Bluetooth capabilities. We faced specific limitations due to our laptop not being Linux, but we were eventually able to identify the Bleak library as a reliable source for Bluetooth capability.
My progress is on schedule, though I anticipate that I will have less time next week due to upcoming exams.
Next week, I hope to assist with the development of the second glove as needed. I hope to also establish Bluetooth communication between the two gloves and the laptop at the same time. I will also hope to figure out what we plan on doing with the speaker. If time permits, I hope to start collecting data from the two-glove setup next week.