There has been a change of plan from what I wanted to do from last two weeks. My original plan was to collect more data for model training, however, our glove needed more fabrication work to ensure the sensors are well attached. We also plan to enhance the glove’s data collection process. Our first set of data (done by Rachel) had to be collected by pressing buttons to determine the time duration of when the data will be read in. So for this week, we are trying to integrate real-time data collection and interpretation. More data is collected for hand gestures in-between each letter gesture (these gestures are mostly random since they are just transitions from one letter to another). A ‘space’ letter is added in case the models can not categorize the ‘random’ gestures well. First round of testing shows promising result. With the random forest model, which has been the model with highest accuracy so far, the accuracy for recognizing these two new labels are quite high.
I also found that with these two new labels added, the neural network accuracy have increased by 10%. This is an interesting finding and I plan to look further into why this is the case. Before this, I have done much tweaking to the model and validating with different hyperparameters, but the network’s accuracy seemed to be capped around 80%, however, with this new dataset, its accuracy reached around 88% and that is something I would like to find out why to help our future classification works.
I would say we are still on schedule since we planned a lot of time for the software implementation. As for next week, I’ll be looking more into the models and working on make our data collection work more smoothly, and if possible, starting to collect data from others.