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Maddie’s Status Report for 3/6/2021

This week I started building our ‘legs’ video library.  I broke down clips from a few different YouTube videos into single repetitions of the exercises.  Originally we had planned to use the video URLs to fetch the exercise clips, but now we think it may be easier to save mp4 versions of the clips, so I am planning to convert some videos to mp4 for trial Sunday and Monday.

I also started initial setup of the Jetson TX2 that we picked up.  I am planning to download OpenPose onto the board over the next few days, per our schedule.  I also spent time working on the design review slides.

I am pretty much on schedule, though I had to add a few extra days in the schedule for board setup and library construction, since it is taking a little longer than I anticipated.

For next week, I want to install and begin testing OpenPose on the TX2.  Additionally, I am planning on working with my team to construct a small video library.  I would also like to get some of the UI created.

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Sarah’s Status Report for 3/6/2021

This week, I manually parsed 3 YouTube workout videos into separate exercise clips (1 repetition each). This gave us 31 individual core and arm exercises for our library. I saved both a gif version of each exercise (using a gif making website) and the start and end times of the exercise from the YouTube videos in a spreadsheet shared with the rest of my group.

I also worked with my teammates on our Design presentation slides and report. We tried to incorporate the feedback we got from our proposal presentation slides. We particularly wanted to make the slides more visual (“marketing visuals”), so I added gifs and more images to the slides.

I requested our TX2 board from the 18-500 inventory, which Maddie picked it up. I also ordered our camera, which is with me. We haven’t met to swap hardware yet. We may need to change the tasks around in our Gantt chart to reflect these changes in task leadership (Maddie for board set up, me more library construction).

I think we’re basically on schedule, though.

Next week, I hope to figure out how we’re going to store the reference exercise clips (either on board, on laptop, or in AWS?). I want to continue parsing videos, too. It takes longer than I thought to find parts of each exercise that loop well, and find the how long to make the clip that will loop. So, I’m planning on parsing a couple videos per week so it’s manageable. I also hope to start working on our scoring algorithm.

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Team Status Report for 3/6/2021

We decided to use a Jetson TX2 Xavier board instead of a Jetson NX Xavier or Jetson AGX Xavier. This is because The 18-500 parts inventory had both the AGX and TX2 boards from previous years. We didn’t want to buy another board if there were already 3 usable boards available. We originally wanted to borrow an AGX, but other groups requested the 2 in inventory earlier than we did, so we weren’t able to borrow them. The inventory still had a TX2, so we submitted a request to borrow that instead, and were able to do so.

Borrowing the boards instead of buying a new one allowed us to stay exactly on schedule. If we bought an NX, then we would’ve been at least 2 days behind because the shipping time is 2 days. It also decreased our costs, so we’re only paying for a camera currently.

To adjust to the board we chose, the TX2, we decided to use a different library: Tensorflow OpenPose. This library was able to analyze some sample images (on Zixuan’s laptop) much faster than the original OpenPose was. While she was testing OpenPose, she was able to verify that the images we took from YouTube videos are usable with Tensorflow OpenPose.

This week, we also worked on our design presentation slides and tried to incorporate the feedback we got from the proposal presentation. So, we added more pictures/visuals, and a testing plan (not actually in the slides, but will be talked about). We’re currently still working on our reasoning for our metrics, but this should be done by the time we present.

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Zixuan’s Status Report for 3/6/2021

This week I successfully installed OpenPose on my laptop and tested it with some images and videos. It took a very long time for OpenPose to run and the frame-rate was only 0.1 fps, so we decided to switch from OpenPose to TensorFlow OpenPose. TensorFlow OpenPose is a library based on the original OpenPose, which lacks some features (such as hand detection) but runs much faster. It is also compatible with TX2, which is the board we decided to use.

After installing tf-openpose, since there are 4 different models included in the library, I ran a set of 50 images with each model to test the runtime and accuracy. It appears that the CMU model is obviously the most accurate one; since we want high accuracy for our project, we will use the CMU model though it takes much longer than the other models.

I also tested tf-openpose with images from different workouts videos to get a better sense of the performance. These results give me a general idea of which poses are harder to detect, and I will try to improve the accuracy by preprocessing the images (rotate, resize, etc.).

I think we are on schedule. Next week I will present our design presentation and work on pose alignment and comparison.