This week we had the Design Presentation and began working on our Design Report. We received feedback from the presentation mainly regarding our design choice to use an LSTM with MediaPipe, where our advisor was a little wary about how well this would work. After discussing it as a group and doing some more research, we are confident that our choice will fit the use-case.
Currently, the most significant risks that could jeopardize the success of our project are semester time constraints. Given that the design review and midterms have taken a lot of time over the past few weeks, and that spring break is coming up, we have not had a lot of time to work on our actual implementation. This is especially concerning given the amount of time it generally takes to train an ML model and the amount of data we need to both create and process. To manage this, we will prioritize training the model based on our neural network groupings, where the networks with less signs will hopefully be quicker to train. Additionally, we will have more frequent group meetings and internal deadlines, so that we can meet all the milestones in the remaining time we have. As for contingency plans, if training the model takes too long we will cut down the number of signs we are including in the platform for quicker training while still maintaining the usefulness of the signs provided to the users.
In terms of changes to the existing design, we realized that utilizing hand landmarks and face landmarks presented some compatibility problems and too much complexity given our current expertise and remaining time. Thus, we removed all signs that involved contact with the face/head and replaced them with other signs (that still involve motion). Because this change was made during our design phase, there are no real costs associated with this change as our chosen signs are still in our chosen datasets and maintain the same level of communicativeness for the user.
Our schedule is mostly the same as before but we plan to make testing data for the model in weeks after spring break and also internally, we plan to devote more effort to training the model.