Our problem space
ExerSkies - our solution
ExerSkies - more information
The user's suggested activity (low intensity) and their daily tracker
Search for your city and get personalized fitness activity based on current weather (i.e. Toronto))
Search for your city and get personalized fitness activity based on current weather (i.e. London)
User can select their desired intensity for their workout
Reflecting on our quarantine experiences, we all shared a similar lack of motivation to commit to exercising everyday, or to leave the house to get some fresh air. With “pandemic fatigue” leading to decreased motivation, our daily scheduled “walk around the block” or “Zoom workouts” never got checked off our to-do lists. Furthermore, with social-distancing measures and the high-stress on health care workers, it’s more important than ever to stay physically active, get fresh air, and remain healthy. Furthermore, we found that many times, our mundane daily routines during the pandemic consisted of sitting in front of our screens either working or doing school. Since there’s little to no variation in our routines, it became difficult to get excited about each day.
What it does
ExerSkies provides insight for the user on their city’s temperature and precipitation. It then uses this data, alongside the user’s preference of workout intensity to help recommend fitness exercises. For example, on a day with a low chance of rain at a temperature of 16°C and an exercise level of medium intensity, it would recommend to stretch and go on a run outside for 20 min. There would also be tips catered to the data, such as “Stay hydrated” or “dress warmly”, and a daily tracker to keep yourself in check. By providing a specific workout suggestion that is different depending on the day and suitable to the current weather conditions, the user is able to introduce variation and thus more excitement into their daily routine. This will hopefully increase their motivation to exercise and build a healthy active lifestyle.
How we built it
To build our app, we first used Geotab Ignition’s Weather base to run a series of SQL queries to obtain precipitation and temperature data within Ontario. We then used Python’s pandas library to aggregate and clean our data. We used Bootstrap, HTML, and CSS to build our front end, and finally Flask to integrate the front-end with the back-end. UI designs were created on Figma.
Challenges we ran into
This was our first time doing both front-end development as well as using Flask, so we were incredibly lost at first. However, through the workshops, mentors, and Google searches, we were able to get a better understanding of HTML, CSS, Bootstrap, and Flask. In order to get the user’s entered city, we also needed to learn about HTTP requests and how to use Jinja2, which were initially quite challenging as well.
Accomplishments that we’re proud of
We were able to create a functional website that had the ability to receive user input through a form, and display data depending on what the user inputted. We’re also proud of our website’s UI design!
What we learned
We learned lots of new skills such as HTML, CSS, Flask, and Bootstrap.
What's next for ExerSkies
We hope to add an additional “transportation suggestion” component, which involves using the current weather data and suggesting a sustainable transportation method for the day. For example, if there was a low chance of rain and warm temperatures, our app could suggest the user to bike to work. Furthermore, we hope to be able to directly link our database to Geotab’s datasets so we can have live updates.