This week, I presented our intended design during the in-class design presentations. We received feedback on the feasibility for our ML models with respect to feature extraction data from MediaPipe being compatible with our models as inputs to LSTM nodes. I reviewed this component of our design during this past week in order to provide adequate justification for the necessity of LSTM cells in our network (in order to support temporal information as part of our model learning), as well as its feasibility (outlining what the input and output data formatting/dimensionality is expected to be at each layer, as well as researching examples of MediaPipe data being used with LSTMs). I also worked more on our code for data formatting (converting landmark data from MediaPipe into numpy arrays that can be fed to our models). I now just need to add resampling from video data to grab the necessary number of frames. We received AWS credits towards the end of this past week, so we have not been able to work much on feature extraction and model training within an EC2 instance. Although, our schedule still indicates we have time for model training, I am a little concerned that we are slightly behind schedule on this front.
In order to catch up, we will hopefully be able to spend more time on implementation once the design report is completed. So far, a good amount of our time has gone towards presentations and documenting vs implementation. Once these deliverables are met, I hope to be able to shift my attention more towards building up our solution.