Tag: Ricky’s Status Report

My main task for this week was to prepare slides for the design presentation. About this, I added more input into which ML models we would use, how much data we would need, how to collect the data, and the general data flow. I also set up GitHub and added the general file structure for the different scripts we would need. This includes gathering data, training the model, testing the model, and the general runtime routine. I also wrote the first draft of the script that is responsible for collecting the data via Bluetooth. I also revamped our entire Gantt chart to reflect the changes to our development cycle (prototypes). I am continuing to think about how we will store the saved models so that different models can be loaded based on the demo goals (NN, SVM, vocabulary)

My progress is on schedule.

Next week, I hope to draft up the scripts for training the various ML models. I hope to also finalize where we will store the models and how to reuse them when they have been thoroughly trained. I will also set up the testing routine to evaluate the performance of the models. I will also explore automatic testing for optimal hyperparameters. I will also assist in developing the physical glove as well as add my input to the way the data should be formatted from the glove

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.