This week I worked on integrating the letter-specific models with our old classifier. The original classifier is responsible for identifying the input data as one of the 26 letters. If the predicted letter is in the list of similar letters that the original classifier easily gets confused on, notably R, U, V, W and M, N, S, T, E, the data will then be passed to one of the smaller models for further classification. This gave slightly better results and was able to make more correct classification.
I performed further data analysis to see if there’s any other way I can improve the model. I did find some data contained invalid values (negative values from flex sensors) that my old outlier removal function did not pick up, hence I was able to refine that to get better data. The sensor values for U and V are rather similar so it could be hard to have a classifier to distinguish between these two. R’s range of values is quite different from the rest of the letters for the middle finger, hence to improve future models for this specific group of letters, I may only train with data of important features, e.g., the flex sensor values.
During this, I also found the ring finger’s flex sensor value for W (finger is straight) is quite similar to that of R,U, and V (finger is bent). Upon further testing with the glove, I found that the flex sensor on the ring finger is not working as intended and is only giving about the same value no matter the bending angle, so we are looking to replace that sensor.
I believe we are still on schedule. In the next week, I’ll be collecting new data after the flex sensor has been fixed. I’ll also be doing some real time testing and experimenting with new ideas of the smaller model (e.g., training on only important features, changing hyperparameter to produce models with less accuracy on the collected data but more generalizability). After that, we’ll be working on our final presentation and report.