Skip to content

A fork version of a project I've worked on

License

Notifications You must be signed in to change notification settings

qstommyshu/MotionMingle

 
 

Repository files navigation

MontionMingle

Developer Names: Qi Shu, Xunzhou Ye, Anhao Jiao, Kehao Huang, Qianlin Chen

Date of project start: 2023-09-16

McMaster University Software Engineering 2023-2024 Capstone Project.

MontionMingle is an innovative online Tai Chi learning platform. It provides a real-time video streaming platform as a web application for both Tai-chi instructors and practitioners. The instructors are able to start a training session and stream their video captured by the webcam. The practitioners are able to join the training session and watch the live video from the instructor.

Additionally, during a training session, all users are able to turn on real-time annotations rendered on the streaming video. In the current system, we implemented three types of annotations, the skeleton, footwork and semantic segmentation. They are generated through the machine learning pipeline running on a server. These annotations are aimed to help practitioners understand and mimic the movement of the instructor, and therefore significantly improve their learning outcomes. Besides that, all of the annotations are user-configurable. Every single practitioner are able to select their preferred annotation to watch, and they are able to seamlessly switch between annotations anytime they want.

Instructor cllient interface:

image

Skeleton annotation: Human skeleton joints to better indicate the Tai Chi instructor’s movement. According to a previous research done by one of our supervisor’s research groups. The skeleton annotation is one of the most popular annotations by the targeted audience group.

image

Footwork annotation: One of the most popular annotations according to the research results. Tai Chi emphasizes whole-body movement. However, when watching an online Tai Chi video stream, it is sometimes hard to tell the center of mass/support foot of the instructor when the practitioner. This annotation helps users to see the Tai Chi footwork part more clearly.

image

Semantic segmentation: According to AOA, human vision peaks between age 19 to 40, after that, elders might start to have trouble distinguishing the background and the instructor. Therefore, this segmentation annotation dims the instructor’s background, and human eyes will just naturally focus on the instructor’s movement to enhance their learning experience.

image

Folders and files structure:

docs - Documentation for the project

refs - Reference material used for the project, including papers

src- Source code

test - Test cases

About

A fork version of a project I've worked on

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • TypeScript 38.0%
  • Python 35.0%
  • TeX 23.4%
  • HTML 2.8%
  • JavaScript 0.8%