Abstract
This paper proposes a new deep learning architecture that aligns human poses to be used in an exercise/rehabilitation assistant system. In short, the assistant system aims to provide users with visual feedback for their physical exercises. The feedback is generated by first extracting a user’s poses while they are performing an exercise through the video feed of the session. The extracted poses are then overlaid with the correct poses and display for the user to observe and fix their errors. This paper focuses on the task of aligning the user’s pose with the correct pose so that they can be overlaid on each other with minimal differences, including scales, locations, and perspectives. We design a new deep architecture to accomplish this task, and show that our methods can effectively reduce alignment errors by 70% on average.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
