Flynt_Synopsis

Making online classrooms mental health friendly with help of artificial intelligence.

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Instructions to run application:

  1. Create a python 3.7 virtual environment and proceed with installation there:

  2. Clone this repository cd to folder and install the modules in requirements.txt and run pip install -r requirements.txt.

  3. To run this project, Open command prompt type

  4. Type python manage.py migrate and then python manage.py run server

  5. You Can See The Website Live in http://127.0.0.1:8000/

  6. Create a new account at the sign up page and avail the services.

Motivation for the project:

The past 2 years of the pandemic have had a very significant impact on our lives both in terms of our physical and mental well-being. Research states that the depression rates in students between ages 10-18 have increased by 72% since the pandemic has started. Therefore to fix this issue, we have created an AI-based online classroom system that monitors various parameters like facial expressions, voice, attentiveness of a student during the class and generates a meta score that gives an overall idea about the mental well-being of the student. Based on this score, the school counselors will be notified and then the student can have a one on one therapy session with the counselor. This system can help identify signs of depression at early stages which makes it significantly easy to help the student in a safe environment.

Tech stack and Modules used:

    React , Flask , Python , NLP ,Tensor Flow , OpenCV , Assembly ai

AI based monitoring processes:

Using an artificial intelligence model. The proposed scheme tracks emotions such as happy,sad,tensed,scared and head,eye,mouth movements, which can be converted into a meta-score based on predefined threshold values selected by the authority who conducts the assessment. The thresholds for the two parameters can be adjusted independently, avoiding false results.

Assembly ai

Using Assembly ai Sentiment Analysis, can detect the sentiment of each sentence of speech spoken in your audio files. Sentiment Analysis returns a result of POSITIVE, NEGATIVE, or NEUTRAL for each sentence in the transcript. We also incorporate these results to our meta-score.

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