Inspiration

One of our teammates saw people with disabilities going to a service center to move and exercise their bodies. They became more energized and engaged with the exercise as they danced with one another while listening to pop music. We thought we could create a website that teaches users how to exercise, detects their body movements, gives feedback about their movement, and generates sounds corresponding to their movements.

What it does

SeeFit teaches a user how to exercise in the right way. Sometimes, we may be unsure whether we’re exercising our bodies correctly. To solve this problem, our virtual coach will give feedback based its detection of a user’s body movement. If a user moves correctly, the coach will give positive feedback, so that the user gradually learns the correct movements. SeeFit also makes exercise more entertaining by providing auditory and visual inputs based on the user’s body movement. Ultimately, we want to add a multiplayer section that allows interaction among different users, with the movements of each user generating sound, and the sounds combining to create music.

How we built it

We built our platform using an open-source library called Openpose; specifically, we used MLI instead of COCO. We also edited some of the functionalities such that it would better fit our purpose. For example, we took out the hand and finger calculations and only focused on the major joints such that the computational speed would be faster since we were mainly focusing on how our general body moves during a workout.

Challenges I ran into

It was our first time using OpenCV. Thus we ran into many issues with different programs such as Caffe. We ultimately had to switch to a combination of OpenCV and tensorflow. This was an arduous process because we had to learn everything from scratch.

Accomplishments that I'm proud of

We were proud that we were able to simulate a squatting motion and also a bicep curl motion using code. We feel that having accurate feedback on our workout form will greatly help with the safety and efficiency of working out.

What's next for SeeFit

Our next step is to work on additional functionalities such as a progress report provided for the user. Furthermore, we feel that a potential native mobile application could be very beneficial to our user base as we will be able to use native capabilities such as the accelerometer, NFC, etc.

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