[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
This project is currently just a proposal.
Please complete the requirements listed on the ideas page and fill out this template.
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 that can identify a person's 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 that 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 https://summerofcode.withgoogle.com|
|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 control 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 2019 with the project E_agri, which is a smart agricultural system based on IoT. 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 and I am currently working on another research paper based on deep learning. I also did an internship at Center for Artificial Intelligence and Robotics (Defence Research Development Organisation, India) where I worked on the development of cost map for a hex-copter using ROS on Jetson TX1 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 other solutions online, so I can get back to the system with a better approach. Since there are a plethora of resources online, about the BeagleBone controllers, Neural Networks and Deep learning, I will be able to find a solution to the problem. If, after multiple attempts, if 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 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. Include quotes from BeagleBoard.org community members who can be found on http://beagleboard.org/discuss and http://bbb.io/gsocchat.
Please complete the requirements listed on the ideas page. Provide link to pull request.
Is there anything else we should have asked you?