There is already an abundance of health-related devices and apps in the market. Still, although exciting at start, people seem to lose interest to the apps after a while and we believe that the reason is that there isn't clear focus and value that they give to the user.

Our product is made with health-care professionals, to both customers and another clinical colleagues in mind to take the app from being both interesting to being potentially life-saving. In the era of personalised medicine, machine learning and information overflow, our product helps to explore your well-being in a natural way.

What it does

1) Visualises health data received from Suunto Movesense and summarises them as recommendations e.g. to relieve stress.

2) Creates additional health information and well-being recommendations by taking advantage of machine learning.

3) Creates advanced summarisation to be used by clinical experts to enable improved healthcare

How we built it

Python, Spark and Azure were used to manipulate the population data from Sitra challenge to create the background to the machine learning model implemented in the application. Java, Javascript, React and Redux were used to build the mobile application and connection to Suunto Movesense device.

Challenges we ran into

The time limit, false fire alarm, lack of sleep…

Accomplishments that we're proud of

Very cool idea, great accomplishments considering the resources available!!!

What we learned

What's next for Columbus

Better implementation. Testing with actual customers. Gathering of more user data to feed it as a null-model to the machine learning implementation to give better recommendations and feature prediction. Better implementation. Testing with actual customers

Share this project: