Marietta, Georgia
March 10, 2024
March 10, 2024
March 12, 2024
14
10.18260/1-2--45529
https://peer.asee.org/45529
213
Over the years I have developed professionally into an aspiring Data Scientist, Machine Learning Engineer, and seasoned Artificial Intelligence Researcher. Currently, I am in the process of publishing a few papers on stress reduction and improving student performance.
More:
Education:
BE in Mechanical Engineering
MBA in Information Technology
MS in Computer Science (IP)
Research interests:
1. Meditation
2. Music
3. AI
Hackathons:
1. INTEL AI Hackathon FIRST prize Winner!
2. Llama 2 ClarifAI LablabAI hackathon SECOND prize winner!
Published papers:
1. FIE 2023 IEEE conference, Texas, USA: EEG Spectral Analysis and Prediction for Inattention Detection in Academic Domain
2. AIMC 2023, Brighton, UK: Introductory Studies on Raga Multi-track Music Generation of Indian classical music using AI.
I’m an Assistant Professor of Software Engineering and the director of the AIET lab in the College of Computing and Software Engineering at Kennesaw State University. With a Ph.D. in Computer Science and prior experience as a software engineer in the industry, my interest in both academic and research activities has laid the foundation to work on advancing educational technologies and pedagogical interventions.
Dr. Md Abdullah Al Hafiz Khan is an Assistant professor of computer science at Kennesaw State University. His expertise is in signal processing and machine learning algorithms. He particularly guides signal processing during the initial phase of the research project.
The demanding nature of engineering education often leads to high stress levels among students, which can impede learning and performance. There is a growing body of literature on how meditation can help reduce stress among engineering students. Meditation, recognized for its stress-alleviating properties, has been shown to lower cortisol levels, improve autonomic balance, reduce sympathetic nervous system activity, and enhance immune function through heart rate variability (HRV) metrics. This study aimed to evaluate the impact of ChakraMarmaKosha meditation (CM-II) on stress reduction among college students. An experiment of 15 students was conducted in a lab setting in which we measured the heart pulse data before, during, and after meditation sessions with a wired earphone-like device. From this, we calculated HRV metrics such as heart rate, Heart Coherence (HC), Standard Deviation of NN intervals (SDNN), Root Mean Square of Successive Differences (RMSSD), Baevsky Stress Index (BSI), and the Low Frequency/High-Frequency ratio (LF/HF ratio) to analyze stress. The results indicated a harmonizing effect on student hearts especially during the meditation. Moving ahead, we would develop a web application for wireless heart pulse data measurement, that facilitates a broader reach of real-time stress monitoring and management in student activities.
Gopi, S., & Dehbozorgi, N., & Khan, M. A. A. H. (2024, March), Exploring the Impact of CM-II Meditation on Stress Levels in College Students through HRV Analysis Paper presented at 2024 South East Section Meeting, Marietta, Georgia. 10.18260/1-2--45529
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2024 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015