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Date: Tuesday, September 20, 2022
Time: 11:00 a.m. - 12:00 p.m.
Location: Klaus 1116 seminar room
Speaker: Negar Tavassolian
Speaker’s Title: Associate Professor
Speaker’s Affiliation: Stevens Institute of Technology
Seminar Title: Ubiquitous Health Monitoring Using Wearable and Non-contact Sensors
Abstract of Talk:
Electromagnetic waves have been employed in various medical settings. The interaction of electromagnetic fields with biological systems can be extremely beneficial and lead to novel applications. In the first part of this talk, I will discuss the use of the millimeter-wave imaging technology for visualizing skin tissues and the detection of skin cancer. I will describe an imaging system with an ultrawide bandwidth using the synthetic ultra-wideband imaging approach, a new ultra-high-resolution imaging technique developed in our group. I will demonstrate that by taking advantage of the intrinsic millimeter-wave dielectric contrasts between normal and malignant skin tissues, ultra-high-resolution millimeter-wave imaging can achieve 3-D, high-contrast in-vivo images of the skin. I will also talk about our group’s work on developing a portable, handheld system for point-of-care imaging and detection of skin cancer using the integrated circuit technology.
Next, I will discuss our research on developing wearable sensing systems with high accuracy for the detection of cardiovascular diseases. There has been significant effort in research and commercial settings on the development of wearable systems for heart health monitoring, in particular devices that monitor cardiac electrophysiology (ECG). However, in addition to the electrical aspects, a perspective on the mechanical activities of the heart and blood vessels also needs to be gained for a comprehensive evaluation of cardiovascular health. For this purpose, other wearable modalities have been recently developed which directly assess the mechanical activities of the heart, i.e. cardio-mechanical sensing. These measurements can be performed with inexpensive and miniature motion sensors, built into small and convenient form-factors. Our approach is to augment cardio-mechanical sensing with more standard modalities such as ECG, and apply sensor fusion algorithms to extract cardiovascular features with high accuracy. The derived features are then analyzed with abnormality detection and classification algorithms to evaluate the wellness of the cardiovascular system and detect diseases. More recently, we have employed the same technology to monitor fetal wellbeing, including the fetal heartbeat and movement, in pregnant women.
Finally, I will talk about our work on developing remote cardiopulmonary sensing systems, which are of critical importance in a variety of clinical and non-clinical applications ranging from monitoring physiological conditions of crew members during space missions to emotion and stress recognition in applications involving human-machine interactions. Our sensing framework involves an optical camera, a depth-sensing camera, a Doppler radar-based system, and a sensor fusion component for the integration of the data received from multiple sensing modalities. As an application, I will discuss our research for crew health monitoring during space exploration missions.
Biographical Sketch of the Speaker:
Negar Tavassolian is an Associate Professor at the Department of Electrical and Computer Engineering at Stevens Institute of Technology in Hoboken, NJ. She received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from Sharif University of Technology (Tehran, Iran, 2003), McGill University (Montreal, Canada, 2006), and Georgia Tech (Atlanta, GA, 2011), and was a Postdoc at the Koch Cancer Institute of MIT (Cambridge, MA, 2011-2013). Dr. Tavassolian was an Assistant Professor at Stevens Institute of Technology from 2013‒2019. She is a recipient of the NSF CAREER Award (2016), the Provost Early Career Award for Research Excellence (2019), the ECE Department Research Award (2022), a senior member of IEEE, and an Associate Editor for IEEE Antennas and Wireless Propagation Letters (AWPL) and the Scientific Reports Journal. Her research includes biomedical imaging, wearable and remote sensing, and machine learning applications in bio-signal analysis.
