Bear_Assays

Back story (💡What inspired us ):

At the end of the day, every human is left with themselves. its not about being alone but feeling lonely. we all would appreciate the help of a good listener. sometimes that is all we need. we can go to a therapist. Yes! we can. we can write it down in a dairy. yes we can but not all times we have time and facility for that. Often we can see it for ourselves how the exercise we do and diet we take affects our mental health. Bear_assays aids you to analyze your mental and physical health. also to help you understand how inter-related they are and cheer you to take good care of them.

👩🏻‍💻 What it Bear_assays?

Asks the user to upload voice notes or write down what's running inside their head
Interprets mood of the user
Asks users to log the list of exercises and amount of sleep or their information from their fitness app(if any)
Uses real world data collected from a smartwatch to help users understand the impact of physical health on mental health
Provides user with a Histogram of their sleep, mood, burnt calories
Calculates the number of calories burnt and displays a number of how much the workout contributed to their mood that day
A mini part of our project is a chrome extension called viraam which any user can download to take a healthy pause from your desk. Viraam makes you to step away from the computer and perform squats/stretching. Viraam helps you in making your workday routine more healthy.

🔨How we built

we used Assembly AI API to convert audio to text
text2emotion python library to extract mood from text
seaborn and matplotlib to display heat-graph(shows relation between physical and mental activity)
Streamlit to integrate everything and make the website

For Viraam

We have implemented pose estimation provided by teachable machine
Developed chrome extension using HTML, CSS and Javascript (tensorflow.js)

Challenges we ran into

Brainstorming was a huge challenge
Almost everything we implemented is new to both of us, so we had rough time learning and applying new libraries
Emotion recognition is the hardest part

🥇 Accomplishments that we're proud of

We are proud of the fact that we have built our first project together and the fact that we learned pretty much everything we implemented in past days only

📖What we learned

Developing applications using steamlit
Planning and Time management

🚀We will develop Bear_Assays further by:

Deploy the streamlit webapp so that everyone can access it
Recommending songs after interpreting mood
Including an option to log food intake
To provide an option to interact with professional mental and physical health guiders
Create weekly and daily dash boards