Faed Ahmed Arnob

Research Engineer

Bio

 

Faed is a CSE graduate from BRAC University Bangladesh. He is a proficient research personnel with research experience in Computer Vision and Machine Learning. Familiar with documentation requirements and capable of bringing an organized and precision-minded approach. Adept at literature reviews and data synthesis and analysis. His research interests also include but are not limited to Extended Reality, Embedded Systems & HCI. Currently working as a Research Engineer at AIMS Lab, UIU. He has also worked as faculty member in Computer Science and Engineering at BRAC University. Besides academics filmmaking is his hobby.

 

Assigned Project

Feasibility Study of the Effectiveness of AR Technology in Assisting Children with Autism
Spectrum Disorder for Literacy.
A Smart, Wearable, Fetal Movement Monitor System to Prevent Stillbirth.

I have profound interest in the intersection of technology and education, particularly in leveraging Augmented Reality (AR) for literacy development in children with Autism Spectrum Disorder (ASD). The feasibility study I am conducting seeks to explore the effectiveness of this AR intervention while gathering valuable insights from caregivers, parents, and educators. By collecting comprehensive data through structured surveys (Likert Scale) and qualitative observations, this study aims to pave the way for a transformative educational approach tailored to the needs of children with ASD. I am also collecting the accelerometer data from the children by providing them a Fitbit wearable during the AR invention period. This shall also determine the engagement of the children during their AR exposure period.

 

This project outlines a comprehensive approach to address the issue of stillbirth through precise monitoring and analysis during pregnancy. By leveraging advanced technology including wearable devices with various sensors, continuous data collection on maternal and fetal health parameters is ensured. This data is then analyzed using sophisticated machine learning techniques to provide personalized risk assessments and interventions throughout each trimester of pregnancy. The goal is to mitigate stillbirth risks, pregnancy complications, and ensure a positive pregnancy journey for women through early detection and tailored care.

 

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