Authorization to allow step and location tracking
Profile Page with image and details
Landing page of Fittr
Tinder card UI
One of the main problems for gym beginners has always been the lack of a support system to keep themselves driven towards their goals. We believe that having someone to share your journey towards a fitter lifestyle is necessary and Fittr provides a great platform for finding the ideal gym buddy.
What it does
Fittr uses a Tinder-esque matching algorithm to match you up with a gym partner, taking into consideration: geographical proximity, weight and one's favourite exercise. The caveat is that the matching process is anonymous until both users have "swiped-right" on each other. Once the user has a match, Fittr keeps you motivated by employing a point based reward system, which pits you in a friendly competition against your partner on a daily basis, based on the number of steps the user walks and his consistency of "checking into" the gym.
How we built it
Fittr is a native iOS application built on a AWS backed server, running on Node.js and MongoDB. We use Google Maps, Cloudinary and Apple CoreMotion APIs to drive different facets of our mobile application. Fittr simulates the Tinder card swiping UI as well as its matching algorithm, to provide a seamless experience to the user.
Challenges we ran into
Initially we faced some hurdles while setting up our remote AWS EC2 cluster. The next big obstacle was to perfect the right-left swiping motion of the profile cards. We solved this by standardizing a deprecated open source library to provide an almost perfect experience.
Accomplishments that we're proud of
- Setting up our remote AWS EC2 cluster
- Integrating multiple APIs to serve our need in different areas
- Replicate the panning gesture of profile cards
What we learned
A robust backend system is essential for any platform, web or mobile and also the importance of constantly coordinating one's efforts with their team members, to meet pressing deadlines.
What's next for Fittr
Fittr is just the beginning of a bigger vision which includes integrating complex workout routines and Machine Learning to provide personalized recommendations, when it comes to finding gym partners and designing workout routines!