Happy-Meter

Isolation in one of the major concerns in this era, what if we made an app to extend a hand to those who are too afraid to reach out for help.

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Inspiration:

Social media is clearly a major contributor of depression and social anxiety, and this has led to the increase in mental illnesses all over the world. The overflowing snippets of lives that people reveal in their stories. We were also inspired by Snapchat and Instagram stories where people showcase their times of happiness, sadness, or sometimes even times of danger.

What it does:

What it does: The application, made using Vision API, aims to monitor and regulate the user’s emotional state by making use of facial recognition to track (anchor) points. The app will first detect the user’s face, then run algorithms to determine the user’s happiness level (from 0-100%). The application will then take the user to a new page, displaying their happiness level visually, and a quote corresponding to the happiness level. When the face is detected to be sad, the app can help provide uplifting quotes to encourage them. When the face is detected to be happy, the app can match the user with motivational quotes. When the face is detected to be below 20% happy, also indicated as the ‘critical’ level, the app can directly provide the phone hotline to the National Suicide Prevention Lifeline

How we built it:

We build this app using Java in Android Studio, with the help of Vision API.

Challenges we ran into:

We attempted to run the code with Google Cloud's Vision API. However, the picture the App takes from the hardware camera only allows us to output low-res thumbnails, so we had to use another camera API to take the picture. We still encountered problems on sending the bitmap over to Google Cloud (spent long hours on this), so we ended up deciding to use a local Android Studio Vision API.

Accomplishments that we're proud of:

We are proud of finding a possible solution to combat depression, one of the world’s most chronic yet worldwide mental illness. We hope that this application can become a stepping stone towards achieving a better mental health.

What we learned:

Ultimately, we learned the value of teamwork, using each team members individual skill sets to the best capacity, coding effectively in a group using Github.

What's next for Mood Detector Android Application:

We personally think that this feature could be an important extension for Snapchat or Instagram stories to address the mental health issue. The app could potentially detect if the user is in distress or in serious peril. In this case proper assistance could be contacted (ex: 911, suicide hotline or even close ones). Therefore, potentially being beneficial for people’s safety and mental well-being. We could take a step further to use database or server to store a list of quotes (eg. SQL or Firebase) to allow the addition and update of quotes seamlessly, without modifying the app itself.

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