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[Proposal for Analysing school students emotions and problems using deep learning algorithm with BeagleBone Black]

A short summary of the idea will go here.

Student: Anirudh Sivakumar
GSoC: [N/A]


This project is currently just a proposal.


Please complete the requirements listed on the ideas page and fill out this template.

About you

IRC: Anirudh666
Github: Anirudh666
School: Manipal Institute of Technology, Manipal
Country: India
Primary language : English, Tamil
Typical work hours : 7AM to 9PM IST

About your project

Project name: Analysing school students emotions and problems using deep learning algorithm with BeagleBone Black


The idea of the project is to make a system which can identify a persons emotions and problems by using audio signal processing on the way they talk and present themselves when interviewed. This system is planned to be used on school students and college students. Mental health is something which is upcoming these days and students suffer from depression and try and hide their feelings from people, because they are too scared to open up. This system helps to identify those people who try and hide their emotions, and can thus be helped in many ways. The project will use aa BeagleBone Black and a microphone circuit to take audio input from the subject, and using Python coding language, a deep learning algorithm will be created accordingly using signal template mapping, which can predict the emotions. The system will be tested on students from schools nearby and can thus be perfected. The data can also be uploaded to a cloud, where appropriate processing can be done and the data can be accessed from anywhere.


Provide a development timeline with a milestone each of the 11 weeks and any pre-work. (A realistic timeline is critical to our selection process.)

Mar 30 Proposal complete, Submitted to
Apr 27 Proposal accepted or rejected
May 18 Pre-work complete, Coding officially begins!
May 25 Milestone #1, Introductory YouTube video
June 1 Milestone #2
June 8 Milestone #3
June 15 18:00 UTC Milestone #4, Mentors and students can begin submitting Phase 1 evaluations
June 19 18:00 UTC Phase 1 Evaluation deadline
June 22 Milestone #5
June 29 Milestone #6
July 6 Milestone #7
July 13 18:00 UTC Milestone #8, Mentors and students can begin submitting Phase 2 evaluations
July 17 18:00 UTC Phase 2 Evaluation deadline
July 20 Milestone #9
July 27 Milestone #10
August 3 Milestone #11, Completion YouTube video
August 10 - 17 18:00 UTC Final week: Students submit their final work product and their final mentor evaluation
August 17 - 24 18:00 UTC Mentors submit final student evaluations

Experience and approach

I have been part of 2 student project teams. I was in the electronics and controls subsystem in [ Formula Manipal] and I am currently the co-founder and electronics and propulsion head in [ loopMIT]. I am also currently in the semi-finals of IICDC 2020. I have a lot of experience in working with electronics and embedded systems. I have also designed a Data Acquisition system using the BeagleBone Black for the Formula Manipal Electric car, which communicated through CAN protocol and acted as the master controller of the system. I am also set to present a research paper at the international conference of ACTSE 2020 at Lucknow and I am currently working on another research paper based on deep learning. Since I have worked with BeagleBone Black before, I will start with designing the hardware part of the circuit, and make a dedicated PCB for it. Then I will start with the coding for the deep learning algorithm, using Python.


If I was to get stuck on a problem, I believe that I will first start looking into all the systems that are linked to that problem and I will read about those systems in detail and see if I can find another solutions online, so I can get back to the system with a better approach. Since there are a plethora of resources online, pertaining to the BeagleBone controllers, Neural Networks and Deep learning, I will be able to find a solution to the problem. If, after multiple attempts I am still not able to correct the system, I will approach one of my faculties at my university, who has worked in this field of embedded systems and deep learning.


If successfully completed, it will help in understanding the emotions of not only students, but all individuals in all fields who are facing problems regarding mental health, and it can help them seek attention. what will its impact be on the community? Include quotes from community members who can be found on and


Please complete the requirements listed on the ideas page. Provide link to pull request.


Is there anything else we should have asked you?