When we were given this challenge of 'maintenance in everyday life', we approached the problem as maintaining users' lives every day. Our inspiration came from the fact that, every day, people are becoming more health conscious.

What it does

The app tracks all activities of the user on a daily basis. It then calculates the time spent by the user on each activity and gives a summary of the users' day. Based on those statistics, the app then suggests improvements to thier lifestyle. Improvements are based on data gathered from Facebook analytics, geo-tracking and thier inputted preferences. For example: If a user spent 50% of his/her day on social media, the app reminds the user to reduce this activity by suggesting to get involved in outdoor activities based on his/her location and preferences. To this effect, the app informs the user about upcoming events he/she is interested in (this is based on his likes/dislikes from gathered data).

How we built it

The prototypes were made with sketch and Invision We built it using Android studio, Machine Learning algorithm (K nearest neighbours and analytical hierarchy process ) connected to Google APIs such as calendar and map and social media APIs. By using this, we built a recommending system for the users.

Challenges we ran into

The first challenge we ran into was coming up with a viable/sustainable business model. This is when we thought, that we will have rewards for each achievement within the app. For example, if the user spent 50% or more of his day moving and involving in some physical activity, he/she gets points and when the said user reaches an exceptional number of reward points, they can use it to get discounts or coupons for some of the services offered by the partner companies. This model thus encourages the user to use the app and achieve his/ her goals. It allows the app to establish a larger user base and allows the partner companies to get more customers.

Accomplishments that we're proud of

  1. Our different angle to the problem
  2. Creating a solution that caters to common people
  3. Arriving upon a model that benefits all by creating personas of different types
  4. Coming up with a viable prototype

What we learned

We learnt how to work in a team under pressure. We learnt that Ecraft is in a very advantageous position because of the immense data that is within their reach. They can make use of this to gain revenue, and more than that, to improve the life of the partners' customers.

What's next for Data driven lifestyle

  • This could be extended and data can be collected from wearable devices like smartwatches to keep better track of users’ activities
  • This data could keep track of heart rate activities and other vitals to connect them to emergency services

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