Sensor-Based Assessment of Motor Complications in Patients with Parkinson's Disease
Behnaz Ghoraani
March 23, 2022, Wednesday, 3:00 PM - 4:00 PM EDT
Parkinson's disease is among chronic diseases that occur more frequently as people age. Parkinson's disease affects over six million globally with estimated costs of about $24,000 per patient, resulting in a total cost of over $20 billion per year in the US, and these numbers are predicted to double by 2040. Both healthcare delivery and quality of life can be improved considerably for the millions of patients afflicted by these debilitating diseases by translating patient's data into clinically actionable information to help the treating physician to optimize the therapeutic interventions according to each patient's disease dynamics. In this talk, Dr. Ghoraani will present her team's novel engineering solutions to address the need for individualized therapeutic approaches in Parkinson's disease. Specifically, Dr. Ghoraani will present her team's progress toward developing new data analytics tools that can be used along with wearable sensors to monitor patient-specific, response-to-medication of patients with Parkinson's disease at their natural environment.
Behnaz Ghoraani received the Ph.D. degree in electrical and computer engineering from Ryerson University, Toronto, Canada, in 2010. From 2010 to 2012, she held a postdoctoral position with the College of Medicine, University of Toronto. She was an Assistant Professor with the Department of Biomedical Engineering, Rochester Institute of Technology, from 2012 to 2016. She is currently an Associate Professor with the Department of Computer and Electrical Engineering, Florida Atlantic University (FAU). Her research interests include generating clinically relevant engineering solutions to tackle significant bottlenecks in data analytics with an emphasis on computer-aided clinical decision making, long-term and continuous health monitoring, remote and personalized therapeutic management, non-stationary and multidimensional signal analysis, adaptive signal feature extraction, traditional and deep learning, and machine learning