Computer and Information Science, University of Michigan - Dearborn Wearable Sensing and Signal Processing Lab

Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to information will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines. Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytics algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary.

I will describe the central theme of my research and my recent research efforts on data driven optimization and knowledge extraction, which targets exploration of low-power architectures, efficient data analytic algorithm design, and system level optimization for medical wearable embedded systems. I will elaborate on my previous, current major active projects on 1) design and development of self-calibrating wearable EEG acquisition systems that operate at the hardware, signal processing and user interface levels, 2) designing wearable sensor networks and biofeedback systems which incorporate sensing, processing, and communications in order to detect activities of daily living in real-time and utilize measures to provide feedback to the user for possible improvement, and 3) the development of highly adaptive, scalable and resilient driver monitoring systems that integrate heterogeneous wearable, in-car, and environmental sensors for monitoring and intervention. This investigation intends to extend and facilitate current infrastructure and vehicle technology to include real-time driver monitoring and feedback in order to integrate them into the information shared between the driver and vehicle (D2V) or driver and infrastructure (D2I). Finally, I will briefly review future prospects of reconfigurable wearable sensors for driver state monitoring.

Biography:

About the speaker: Omid Dehzangi received B.Sc. and M.Sc. degrees in Computer Science and Engineering from the School of Electrical and Computer Engineering, Shiraz University. He also received his Ph.D. degree from the School of Computer Engineering at Nanyang Technological University.  In 2013 and 2014, he completed postdoctoral fellowships in the Center for Brain Health and the Department of Electrical Engineering at the University of Texas at Dallas, respectively. Omid Dehzangi is currently an Assistant Professor in the Department of Computer and Information Science at University of Michigan-Dearborn. His research interests lie broadly in the area of wearable embedded systems, their signal processing and data analytics algorithm design with the emphasis on medical applications. His research has been funded by the NSF, NIH, DoD (TATRC), SRC and industry (Toyota, Ford, Texas Instruments and Samsung). He has more than 70 published articles in peer reviewed journals and conferences.