Tyde (Team: Dscubed In Pyjamas)

Going with the flow

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Inspiration

  • Current period tracking apps only specialise in period and sexual health while exercise apps such as Nike Run Club provide general fitness plans, but nothing catered to women - in particular, the phase at which a woman's body is in.
  • We wanted to create a centralised hub that filled the gap in the competitive market by catering to the four phases (period, follicular, ovulation and luteal) and recommending specific workout plans according to their fitness goals.
  • This is an app that caters to the needs and wants of a modern woman, optimising their physical and menstrual wellness.

What it does

  • We track and predict the users menstrual cycle through the tracking and calender pages. We then take into account their fitness goals to recommend a specific workout routine. E.g. If they are in their ovulation phase, their body is able to handle intense exercise such as hiit workouts, therefore we will recommend a weekly plan according to this knowledge.

How we built it

The app is built using React Native for the frontend, providing a cross-platform mobile user interface. Node.js serves as the backend environment, likely handling API requests, business logic, and server-side operations. Google Cloud Functions (using Python-based Jupyter Notebooks) are used to deploy serverless functions that handle specific backend tasks for data processing or interacting with user data from the frontend.

  • To build the Machine Learning model, we used Sklearns NeighbourRegression with k = 3 and we also used a ChatGPT api to run the in app chatbot.

Challenges we ran into

  1. Our fourth teammate dropped out on the second day. Really pushed us back technically.
  2. Making the ML model especially since we only had such a short amount of time, we needed to come up with a quick but efficient solution.

Accomplishments that we're proud of

  1. Really proud of Sahil for handling the front end and all technical challenges
  2. Really proud of Soaham for being curious and willing to pick up new skills under high pressure
  3. Really proud of Danielle for stepping up and rolling with the punches

What we learned

We learnt that communcation is key. Not one person can handle everything but as long as we are reliable and collaborative, under pressure we can always make things work. We also learnt that app development and machine learning isnt as easy as what youtube makes it seem.

What's next for Tyde (Team Dscubed In Pyjamas)

  1. better ML algorithm - ARIMA time series most likely
  2. better integration of the front and back end
  3. Monetisation? extra features and pay the ChatGPT API

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