Bio

Samira Shamsir received her B.Sc. degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET) in 2015. She started her doctoral degree at the University of Tennessee, Knoxville in 2016 under the supervision of Dr. Syed Kamrul Islam. She was a Min H. Kao Fellow during her study at the University of Tennessee, Knoxville. She transferred to the University of Missouri, Columbia in 2018 and received her Doctor of Philosophy (Ph.D.) degree in Electrical and Computer Engineering in 2020. Her dissertation topic includes the modeling of gallium nitride transistor for high power and high frequency applications. During her graduate study, she was also involved in integrated circuit design for biosensor applications. She has received the IEEE-IMS Graduate Fellowship Award from the IEEE Instrumentation and Measurement Society in 2019. She has also received the Outstanding Ph.D. Student Award from the college of Engineering at University of Missouri, Columbia in 2020.

Abstract

Smart Infant-Monitoring System with Machine Learning Model to Detect Physiological Activities and Ambient Conditions

In this presentation, a smart infant monitoring system will be presented that has been developed to detect various physiological functions using multiple non-invasive sensors. Non-invasive evaluation of vital signals is a new frontier of patient health monitoring. In particular, for health monitoring of premature infants, it is critical for the sensor-device to be non-invasive in nature. In addition, a continuous and remote monitoring system is highly desirable so that the caregiver is not always required to be present in person to monitor the infant. In this view, the proposed system can evaluate different physiological activities such as respiration, movement, noise, position, as well as ambient temperature, and humidity using non-invasive sensors. By processing the acquired data from different sensor modules, the system can generate alarm signals for adverse situations such as the occurrence of apnea, seizure, or noisy and uncomfortable environmental conditions. The system is also able to detect critical respiratory conditions by analyzing breathing data and saturated blood oxygen level (SpO2) using machine learning (ML) models such as neural networks. The proposed system allows the caregiver to monitor the condition of the patient from a remote location by implementing wireless communication with a remote computer or a cell phone.