Rose Faghih, University of Houstin, “Wearable-Machine Interface Architectures for Neurocognitive Stress”

When:
September 3, 2019 @ 11:00 am – 12:00 pm
2019-09-03T11:00:00-04:00
2019-09-03T12:00:00-04:00
Where:
Great Hall (Levering)

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Bio

“Wearable-Machine Interface Architectures for Neurocognitive Stress”

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. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from MIT, where she was a member of the MIT Laboratory for Information and Decision Systems as well as the MIT-Harvard Neuroscience Statistics Research Laboratory. She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory at MIT. Dr. Faghih has won multiple awards, including the 2016 IEEE-USA New Face of Engineering award. Her research interests include control, estimation, and system identification of neural and physiological systems. For more information, please visit the lab’s website: http://ComputationalMedicineLab.ece.uh.edu/

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Abstract

“Wearable-Machine Interface Architectures for Neurocognitive Stress”

The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse impulsive events as inputs to the model. Then, we recover the timings and amplitudes of sudomotor nerve activity using a generalized cross-validation based sparse recovery approach. Subsequently, we relate arousal to the probability that a phasic driver impulse occurs in skin conductance signals to continuously track a neurocognitive-stress-related arousal level. Finally, we close the loop using fuzzy control. In particular, we design excitatory and inhibitory wearable machine-interface architectures to regulate neurocognitive-stress-related arousal. Results demonstrate a promising approach for tracking and regulating neurocognitive stress through wearable devices. Since wearable devices can be used conveniently in one’s daily life, wearable machine-interface architectures have a great potential to monitor and regulate one’s neurocognitive stress seamlessly in real-world situations.

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JHU - Institute for Computational Medicine