Mood-o-Meter (Team - Sinister Bots)

Demo of the results.
Demo of the Angry face
Demo response of the sad face

Inspiration

According to annual report of “Labour Force Survey” from 2019, 44% of the work related illnesses and 54% working days are lost due to stress, depression or anxiety. Another survey by “The American Institute of Stress (AIS) “, 19% or almost one in five respondents had quit a previous position because of job stress and nearly one in four have been driven to tears because of workplace stress. Stress is one of the main contributing factor for modern illnesses and we need to keep stress in check in our day to day life. And seek medical help if necessary.

What it does

Introducing ML-based stress reduction system that will detect emotions and will give suitable response to deal with bad emotions Our system is capable of detecting 7 different kinds of human emotions: Angry, Disgust, Fear, Happiness, Sadness, Surprise, Neutral

How I built it

Using Machine learning to detect emotion and then built a wrapper to respond accordingly.

Challenges I ran into

Data collection is the biggest challenge that I encountered.

Accomplishments that I'm proud of

Collected data from internet made the model and trained it in less than a day.

What I learned

Team building and team management.

What's next for Mood-o-Meter

Can be integrated with AI based personal assistance systems.

Built With

artificial-intelligence
django
machine-learning
python

Created by

I worked on the back end and front end part of the project in which i made use of django-python, and definitely HTML 5 CSS. This was my first hackathon. Enjoyed alot, learn so many things skill enhancing, team formation, and team management.

Aarti Ravariya
I was involved in brainstroming, providing guidance to the team and organisng the project presentation.

Sandeep Brahmadev Kadam
I worked on Machine learning part we built a model that detects the human face and identifies the emotions. The model can detect upto 7 emotions. We got a training accuracy of 97 % and testing accuracy of 65 %.

Vishwas 27
Being the Team Lead, I worked on every modules of the project majorly guiding my teammates for successful completion of the project. Apart from team management I worked in Machine Learning domain to develop a neural network to detect emotions using facial features.

Saksham Jain