Our interpretation of depth is to dive deep into our own lives and connect with issues high school students face. Essentially our interpretation of depth creates a unique opportunity to reflect on our lives as high school Students. We can understand and connect with the most prominent needs of High School students regarding health, productivity, and mood cycles.
What it does
Displays mood entries with dates and ratings backed up by a database storage. Promotes emotional reflectiveness by monitoring feelings over time and identifying patterns in mood. Any entry can be edited based upon further reflection by the user if required. Any entry can be accessed and deleted. The mood entries also show a counter of feelings over past entries effectively causing the user to reflect and initiate change in their lives.
How we built it
We built our project using java and mysql. The UI frontend is done in Java and using eclipse' window builder. A database is created and manipulated using phpmyadmin, and mysql.
Challenges we ran into
The main challeneg was connecting Java with MySql and the failed idea of using springboot.
Accomplishments that we're proud of
We are proud of learning how to use databases along with displaying the data inputs through a good Graphical User Interface (GUI).
What we learned
We learned how to create a database, and build products that solve problems.
What's next for VitaMood
So they're many improvements that need to be made to make this prototype into a impactful product. We want to add functionality such as: recommendations, sharing features, data visualization, and sentiment analysis. We would analyze patterns in the end users moods to give daily suggestions such as “Ask for help” upon seeing a high trend in depressive moods. Also, we would want to display graphs, to help the user also see trends in his mood.Furthermore, we want to be create a community of people to help every user and this will be done through allowing a sharing feature to invite friends to help with the process! Lastly, we want to leverage ML to output moods based of different data.
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