This week, I primarily focused on researching how the ML model will be incorporated into the design. The first major issue I looked into was the logistics of collecting the training data. Recall that we will be collecting our dataset to train the model. Past similar projects don’t offer too much information about this. Instead, I came up with the idea of using a Python script to automate collecting sensor readings while another person signs a specific word. Then, after exporting to an Excel file, we can go back and systematically label all the data with the specific word label. This is probably the plan for now and will be adjusted based on its performance when we get to that stage. I also considered additional processing on the input before the ML. Past projects seemed to preprocess the flex sensor output to a predetermined range of angle values. I believe that this is something worth looking into and will add that to our design, and have the raw input as a backup plan. Processing outputs is something I also took inspiration from previous projects. They used a general heuristic of requiring repeated patterns of the same predicted word before speaker output. I believe that this idea will also help us cut down false positives when the signer is in a transition state. In summary, I was able to narrow down the complexity and logistics of using the ML model for prediction.
My progress is on schedule. I am confident that I will wrap up the planning and design logistics soon.
Next week, I hope to present my ML findings to my partners and start working on the design slides that are focused on the ML model. I will also look into writing a Python script for data collection based on the glove compute that we decide on. I will also need to look into Bluetooth modules for Python.