Cooper Status Report #1 (2/16)

This week we were working on the overall design of our project, trying to get into the specifics about how the different portions of our project were going to meld together and communicate with each other. My primary project this week was deciding on and ordering the hardware we needed for a successful project. Specifically we had to decide on a camera to use and a conduit to communicate camera data to the cloud. We wanted our conduit to have wifi connectivity such that it could communicate with the cloud without a middleman or an ethernet cord. We also wanted it to be bluetooth capable to allow for flexibility in our design to potentially have the conduit communicate directly with the user phone if we decided we wanted to (potentially for offline storage/usage when disconnected from wifi). With this in mind my team and I chose the Raspberry Pi 3 B+ as it had all of the features we wanted as well as our team having a good amount of experience and comfort working with Raspberry Pi’s. Next I had to decide on a camera. First I had to make a decision about whether or not we needed a depth camera, I was looking at the Kinect and the Intel RealSense D415 for depth cameras. Ultimately I determined that a depth camera was necessary as the video processing done by OpenPose is not helped by depth cameras. Looking through other options for cameras I narrowed it down to the Logitech C290 and the Raspberry Pi Camera V2. The specs of the camera were relatively similar and it was unclear that we were going to be able to get any kind of performance increase from one or the other so I chose to go with the Raspberry camera as it was slightly cheaper and there would be more available documentation if we ran into any problems.

For the coming week we will be (hopefully) receiving our Pi and Camera. I plan to start work on our classification algorithm. Using the output data from OpenPose I would like to use a KNN map algorithm to identify workouts. In the first week working on this I hope to get a KNN algorithm working and running on an existing dataset that likely will not be particularly useful for us. This should give me a better sense of the functionality of the KNN algorithm and confirm it is the one we should be using, while also being able to quickly switch it to another dataset once we have accumulated an appropriate number of datapoints. At the same time, I will use the camera and possibly publicly available videos of workouts to begin compiling an extensive dataset for our particular problem. Hopefully by the following week I can begin to attempt training the algorithm on our new algorithm, or possibly move on to a different algorithm if I determine that KNN is not appropriate for this problem.

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