This week, I did a lot of group work.
I worked with Anushka to set up the Jetson Nano. We had some trouble setting up WiFi, but eventually figured out how to connect to CMU-DEVICE. I plan to set up the MQTT broker on the Jetson in order to improve WiFi latency. Since the broker is closer to the wearable and on the same network, it should be much faster in theory. This weekend, I plan to test this out and see if there is any noticeable speedup in data transmission.
We decided on training a machine learning model to classify the number of fingers on the forearm, since fitting a curve and finding the min and max to classify swipes and pinches was too complicated. So, I worked with Anushka and Joanne to create a SVM classifier to classify the number of fingers. I wrote a training script and trained it on data we had collected a while ago and got about 95% accuracy in cross validation! It seems to perform well on live data, but it isn’t perfect. SVM seems to work well for this task, so we collected some more data and are training it to see if we can get any improvements. The next steps are to improve finger detection given that we know the approximate number of fingers and then improve gesture detection given hat we know the approximate locations of the fingers. I am not too confident that finger detection will be as good as we want, so we may have to deal with that this week when we actually fully implement it. Maybe we need to apply some filtering technique or throw out bad detections.
I also further integrated the wearable with Joanne’s Webapp. The improved finger detection powered by the SVM seemed to be better. Less swipes are classified as pinches which means the Webapp consistently rotates the model.
I am on track for this week, but things are piling up…