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Inspiration

The number of cases have increased in adults facing anxiety, fatigue and depression during the pandemic vs. before the pandemic.

What it does

A platform to help individuals with lingering fatigue and other mental symptoms that they experience during the pandemic through a number of interactive pages that motivate self awareness and health. The pages are:

1) Welcome page

2) Login Page Username & Password

3) Create an account: Full name, Username, Password, Location (for connecting with the community), Interests (Food, Entertainment, Animals, sports, travel locations), Contacts(Friends and Family), Have you been affected by COVID19 before? and When?

4) Homepage Buttons leading to different pages: Connect, Exercise, Educating, Diagnosis, Notifications(Motivational, Check-in current Feelings with Emojis), Update info, Achievements(Badges, Stickers, Levels), Devices(Connect to apple watch)

5) Connect Page: Links with other users, Family and friends depending on what the user feels

6) Exercise(meditation, yoga)

7) Educating (difference between anxiety, fatigue and depression)

8) Diagnosis(Through voice and image recognition detect how the user feels and lead them to the Educating page or Exercise page, also, through user input of how they feel they get a diagnosis of whether they carry covid or not)

9) Notifications(Set up, Check-in, Motivational quotes): Based on what the user feels?

10) Achievements: Earn badges through completing tasks. A page to show statistics of their activities (maybe exercise or how they’ve improved)

[Seniors : voice commands (machine learning detecting the tone of their voice to assume an expression), light and dark background options]

How we built it

We built the prototype using Adobe XD.

We attempted to build the webinterface using HTML, CSS, and Javascript.

Machine Learning: 1)Covid detection through symptoms input: COVID-19 Symptoms Checker Dataset using Logistic Regression, Random Forest Classifier, SVM

2) Feeling detection through 2.1) Facial expression: Fer2013 Dataset using resNet50, CNN 2.2) Voice input: Emotion Sensor Dataset using NLP