This week I focused on testing the finalized comparison algorithm and collecting data to make an informed decision as to which algorithm to use for the final demo. We ran comprehensive testing on five different algorithms (DTW, FastDTW, SparseDTW, Euclidean Distance, Velocity) and collected data on the performance of these algorithms on capturing different aspects of movement similarities and differences.
Throughout this project, two major things I had to learn was Numpy and OpenCV. These tools were completely new to me and I had to learn them from scratch. OpenCV was used to process our input videos and provide us with the 3D capture data, and Numpy was a necessary library that made implementing the complex calculations involved in our comparison algorithms much easier than it otherwise would have been. For OpenCV, I found the official website extremely useful, with detailed tutorials walking users through the implementation process. I also benefited greatly from the code examples they posted on the website, since those provided a good starting point a lot of the time. In terms of Numpy, I resorted to a tool that I have often used when trying to learn a new programming language or library: W3Schools. I found this website to have a well laid out introduction to Numpy, as well as numerous specific examples. With all those resources available, I was able to pick up the library and put it to use relatively quickly.