Hamidur Rahman is a doctoral (PhD) student since November, 2015 in Intelligent Systems group of Innovation, Design and Engineering (IDT) in Mälardalens University. He finished his Licentiate degree in June 2018. The title of his Licentiate Thesis was An Intelligent Non-Contact based Approach for Monitoring Driver’s Cognitive Load. He is planning to defense his PhD thesis in February 2021.
Hamidur's main research area is Artificial Intelligence mainly machine learning, deep learning. At present, he is a last year Ph.D. student in computer science, and in the last 5 years, he has conducted research and developed methods for non-contact vision-based solutions in health monitoring, driver monitoring, indoor navigation using machine learning, deep learning, image processing, and computer vision technology.
In his Ph.D. thesis, he has used the driver’s facial image sequence to extract physiological parameters for the identification of driver cognitive load. The significant contributions of his thesis include data collection using a non-contact sensor (i.e., camera sensor), feature extraction using facial images, and the development of machine learning and deep learning models for cognitive load classification. He has also worked on other projects such as INVIP: Indoor navigation for visually impaired using deep learning, Into DeeP: Deep learning for industrial applications, and HR R-peak detection quality index analysis: Non-contact based heart rate R-peak detection using facial image sequence. He worked on the 3D-image reconstruction for forwarding camera motion In his master's thesis, .
During his doctoral study, he has worked on several projects collaborated with the Volvo Car, Toyota Europe, The Swedish National Road and Transport Research Institute (VTI), SenseAir, Autoliv, Karolinska Institute, Prevas, and Anpasarna. He has published scientific articles in international journals and both international and national conferences. He is also involved in teaching and developing courses and course materials. He has experience supervising bachelor and master student’s thesis.
Machine Learning for Cognitive Load Classification – a Case Study on Contact-free Approach (Aug 2020) Mobyen Uddin Ahmed, Shahina Begum, Rikard Gestlöf , Hamidur Rahman, Johannes Sörman 16th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2020)
A Vision based Indoor Navigation System for Individual with Visual Impairment (May 2020) Mobyen Uddin Ahmed, Mohammed Ghaith Altarabichi, Shahina Begum, Fredrik Ginsberg , Robert Glaes , Magnus Östgren , Hamidur Rahman, Magnus Sörensen International Journal of Artificial Intelligence (IJAI)
Non-contact-based Driver's Cognitive Load Classification using Physiological and Vehicular Parameters (Nov 2019) Hamidur Rahman, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum Biomedical Signal Processing and Control (BSPC)
Non-contact Physiological Parameters Extraction using Facial Video considering Illumination, Motion, Movement and Vibration (May 2019) Hamidur Rahman, Mobyen Uddin Ahmed, Shahina Begum IEEE Transactions on Biomedical Engineering (TBME)
Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination (Jun 2018) Mobyen Uddin Ahmed, Hamidur Rahman, Shahina Begum 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health (pHealth2018)
Vision-Based Remote Heart Rate Variability Monitoring using Camera (Oct 2017) Hamidur Rahman, Mobyen Uddin Ahmed, Shahina Begum 4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)
|HR R-peak detection quality index analysis||finished|
|INVIP: Indoor Navigation for Visual Impairment Persons using Computer Vision and Machine learning||finished|
|SafeDriver: A Real Time Driver's State Monitoring and Prediction System||finished|
|Deep Learning based Eye Tracking and Head Movement Detection||available|
|Remote monitoring of physiological parameters using facial images||available|
|Smart Mirror to monitor Health Status using Biological Signals||available|
|Human Emotion Detection using Deep Learning||in progress|