Artificial Intelligence (AI) for Healthcare Workshop: Wearable Computing for Mental Well-Being
Bio: 
Dr. Rose T. Faghih is an assistant professor of Electrical and Computer Engineering at the University of Houston where she directs the Computational Medicine Laboratory. Dr. Faghih has won multiple awards, including the National Science Foundation Graduate Research Fellowship, an MIT Graduate Fellowship, the University of Maryland President’s Scholarship, and the University of Maryland’s Department of Electrical and Computer Engineering Chair’s Award. In 2016, Dr. Faghih was selected by IEEE-USA and DiscoverE as one of the “ New Faces of Engineering ” for her work on neuroendocrine hormones. She has also been inducted into various honor societies including Phi Kappa Phi, Tau Beta Pi, and Eta Kappa Nu. Dr. Faghih has been writing poetry since first grade, and has published two poetry books. She also enjoys creating digital arts, and has exhibited her artwork at the MIT Graduate Art Soiree.
Abstract (for award 2226123):
Smartwatch-like wearables have enabled seamless tracking of vital signs and physical activities, but still lack a significant feature: they are currently unable to provide any information about brain states or to modulate brain function for optimizing human health and performance. This project aims to make it possible for wearables to feature such capabilities. Being aware of brain states is not only extremely valuable in clinical studies but is also crucial to improving human performance in various everyday life activities. While recording neural signals directly from the scalp region is possible, it is impractical for use in everyday life. In order to fill this gap, the goal of this project is to pioneer a closed-loop brain-aware wearable architecture called MINDWATCH. This enables (1) decoding multidimensional brain states from noninvasive wearable devices and (2) applying corrective control. MINDWATCH will transform healthcare delivery (e.g., aging, autism, dementia) as well as human performance and productivity enhancement (e.g., online learning, smart workplaces). For instance, knowledge of mental health and cognitive engagement can enable detecting if a student is depressed or is not cognitively engaged/learning, which makes it possible to take corrective action early on. The research is integrated with educational and outreach activities with an emphasis on increasing the participation of minorities in science and engineering. These activities include hosting hands-on STEM K12 events, supervising undergraduate research interns and capstone senior design projects, creating educational videos, and interdisciplinary course development.
This project seeks to overcome the barriers to achieving brain-aware wearables by pioneering a transformative system-theoretic computational toolset for noninvasive closed-loop wearable architectures that monitor and modulate brain function without needing neural recordings. The proposed framework will (1) infer discrete brain-related events in real-world settings, (2) decode multidimensional latent neurobehavioral states based on inferred brain activity, and (3) apply robust adaptive control to maintain the neurobehavioral states within desired ranges. The closed-loop framework will be rigorously validated using experiments on interactive human-technology environments and mental health. The proposed research will provide foundational statistical signal processing and control-theoretic tools for the analysis of multimodal binary and continuous physiological observations that occur on multiple time-scales. While the initial focus is on two aspects of brain function, namely mental health and cognitive engagement, the proposed framework opens up the opportunity to investigate broader questions in computational neuroscience.