In my penultimate week of my summer research, I invested a good amount of time looking into alternative gesture classification methods. Using past work our postdoc, Naomi, had done during her PhD as an example, I wrote a python script to preprocess our IMU gesture data. Instead of raw data, I now have a general csv that has extracted features as attributes. I then used this processed data in SVM and decision tree models (using sklearn packages). To our excitement, these models did much better than the previous real-time models using hmm! After implementing a kfold cross validation technique and plotting results on confusion matrices, both models classified 100% accurately on 8/10 gestures (the other 2 had some trouble, but were still above 80% accuracy). This is a great basis to work off of going into my final week. Hopefully I will collect more data from other lab members so as to build more robust models.