Negar Ebadi

Negar Ebadi


Stevens Institute of Technology





Negar Ebadi (formerly 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 Institute of Technology (Atlanta, GA, 2011). She was a Postdoctoral Associate at the David H. Koch Institute for Cancer Research at Massachusetts Institute of Technology (Cambridge, MA, 2011-2013). Dr. Ebadi 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 the IEEE Antennas and Wireless Propagation Letters (AWPL) and the Scientific Reports Journal. She is a Technical Program Committee member of IEEE MTT-10: Biological Effects and Medical Applications of RF and Microwaves. Her research interests include electromagnetics, wearable sensing, biomedical imaging, and mobile health.


Ubiquitous Health Monitoring Using Wearable and Non-contact Sensors

Electromagnetic waves have been employed for various biological and medical applications. The interaction of electromagnetic fields with biological systems can be extremely beneficial and lead to novel medical 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 millimeter-wave 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, including valvular and peripheral arterial diseases. There has been significant effort recently on the development of wearable systems for heart health monitoring in research and commercial settings, 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. Two such modalities are seismo- and gyro-cardiography, the measurements of linear and rotational heartbeat-induced chest movements, respectively. These measurements can be performed with inexpensive and miniature accelerometers and gyroscopes, built into small and convenient wearable form-factors. Our approach is to augment cardio-mechanical sensing with more standard modalities such as ECG and photoplethysmography (PPG), and apply sensor fusion algorithms on the multi-modal signals to extract cardiovascular features. 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 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 interaction. 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 by NASA.

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