Finding means to have a healthy way of life and exercising regularly is a constant issue for a lot of people. Many of us felt the difficulties that the COVID pandemic imposed on our lives, as the restrictions caused gyms to close and interrupted group sports activities, forcing us to look for alternatives. Some research also points out that group exercises and exercise communities can help boost people’s participation by providing regular schedules and motivation.
For the PoC we chose the case of workforce exercise referred to as “labor gymnastics” programs frequently offered at companies. The basic idea is to provide an app that both trainers and participants can use to participate in an exercise session. The app would allow the participants to move around and visualize the trainer's movements from different angles. The instructor's movements will be captured from the instructor's mobile phone camera and projected to the students’ cellphones as an avatar. Using cameras to track movements is not the best method, but for this proof of concept, the goal was to explore different technologies.
To achieve that, there were a series of challenges that needed to be addressed. The following list provides a description of each of those challenges, the solution we used and some alternatives.
In general, the technologies used proved to be ready to deliver a good and immersive experience. Without too much effort we were able to create a simple Gym application. Unity and its assets (Mirror and Agora) abstract a lot of the complexity behind the features, making the development process easier, allowing more configuration and less coding. For example, to include a video on a TV inside the Gym we just had to drag the video file inside the TV model.
MediaPipe, even with the difficulties reported above, proved to be fairly accurate when it came to identifying the instructor's movements, and it tends to get better as BlazePose GHUM Holistic, a new algorithm focused on fitness tracking, is being released. On our tests, MediaPipe was able to properly recognize a variety of positions as demonstrated on the video below. It is important to note that we did a basic mapping of the MediaPipe information to the avatar model, and we also did not implement finger tracking, so some inconsistencies in the movements were expected.
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