Team Report #1 (2/16)

Significant Risks

  • Speed of OpenPose and network communications must be fast enough to provide real-time updates and form correction.
    • Security is of minimal concern here which should help optimize our communication.
    • Wifi-enabled conduit eliminates middleman
    • OpenPose on AWS machines should be extremely fast
    • Potential Workaround: Provide feedback at the end of the specific workout
      • Gives us a significant buffer (~30 seconds to a minute) before the user needs the feedback
  • Cloud Security
    • Our connections between the cloud and devices need to be secured. Our implementation will take security measures to ensure that our cloud endpoints are only being used for their intended purposes


Design Changes & Decisions

  • Raspberry Pi 3 B+ as Conduit
  • Raspberry Pi Camera V2 for Camera
  • Classification algorithm will be run on OpenPose Data not directly on video/image files
    • Pro: Should make algorithm itself much simpler and potentially more accurate
    • Con: Need to compile large dataset
      • Run OpenPose on pre-existing (online) workout videos to help compile more data than we could by creating our own.
  • We are no longer using a RGB-D camera and rather just RGB
    • We believe that the added depth will not improve openpose’s performance and is not needed
  • Classification will have to occur on images rather than videos in order to be able to classify an exercise without missing the first rep

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