This week, we worked more on the gesture recognition algorithm. We figured it would be best to go back to the basics and figure out a more concrete finger detection algorithm, then develop a gesture algorithm on top of that.
Currently, we abandoned the finger detection algorithm in favor of tracking either mins or maxes in the signal, then determining the relationship between them and give a metric to Unity to perform a translation. However, this metric is highly inaccurate. Edward suggested using SVMs for finger detection. The difference between pinches and swipes are the number of fingers present, so we can use existing data to train the model so that it can tell us which sensor represents one, two, or no fingers.
I added some new data that is more comprehensive to the existing data set. I also added some noise so that the model would also know what to classify if there is too many distractions.
Afterwards, I trained the data using different subsets of the new data combined with the old data. The reason behind this was because training the new data took a lot of time. It took over 4 hours to train 200 lines of new data and a lot of power.
Next week, I’m going to train the model on the Jetson with all the data. Jetsons are known for high machine learning capabilities, so maybe using it will make our computation go faster. We managed to add wifi to the Jetson this week, so it’ll be easy to download our existing code from Github. I am concerned about training the new model with the new data. With only the old data, we have a 95% accuracy, but hopefully with the new data, we’ll be prepared for more wild circumstances.
I think I’m personally back on schedule. I want to revisit the hologram soon so that we can complete integration next week. I’m excited to hear feedback from interim demo day since we have time for improvement and will likely add or improve parts of our project after Monday.