It is no secret that we are facing a mental health crisis, exacerbated by the COVID-19 pandemic. Statistics Canada reported a 20% reduction in youth aged 15-24 reporting excellent or very good mental health after the pandemic started, with a particularly heavy impact of LGBTQ peoples and visible minorities. In trying and distant times, affordable mental health resources have grown increasingly difficult to access, creating a challenge for those in desperate need of help. We decided to help address this with Amumu.

What it does

Amumu is an Artificial Intelligence based mental health tracker, monitor and advisor which allows users to submit daily entries regarding their mental health. Using sentiment analysis, an emotional score is determined and the user can keep track of their emotional health history and trends. Daily advice is provided based on the user's trends to assist them on their mental health journey.

How we built it

First the design was done on Figma. The interface was built using react bootstrap. We used express and MongoDB to create the database and API. We use a sentiment analysis library to analyze journal entries.

Challenges we ran into

  • Time constraints
  • remote collaboration
  • learning new technologies such as Express, MongoDB, Figma
  • sentiment analysis.

Accomplishments that we're proud of

We're proud of the UI/UX, our collaborative effort, design thinking and problem solving. Another thing we are proud of is successfully implementing sentiment analysis.

What we learned

During the hackaton we learned a lot of new skills. The most significant was working a group remotely. Another thing we learned was using express and MongoDB to create the API. For the design of Amumu we learned to use Figma to model the app. Finally we had to do research for sentiment analysis for the main functionality of the app.

What's next for Amumu

  • Improved Text to Emotion Analytics
  • Mobile App
  • Finish Web Implementation
  • More mental health resources
  • Add more features for the user's to track

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