Dynamic Systems Modeling of Humans to Optimize Digital Health Care

Speaker: Misha Pavel, Professor at Northeastern University; Visiting Professor, UC Davis

Speaker bio: Misha Pavel, Ph.D., holds a joint faculty appointment in Northeastern University’s Khoury College of Computer Sciences and Bouvé College of Health Sciences and visiting faculty at UC Davis. His background comprises electrical engineering, computer science, and experimental psychology. His research includes multi-scale dynamic computational modeling of behaviors and psychological states, with applications ranging from elder care to augmentation of human performance. Pavel uses these model-based approaches to develop algorithms transforming unobtrusive monitoring from smart homes and mobile devices to practical and actionable knowledge for diagnosis and intervention. Under the auspices of the Northeastern-based Consortium on Technology for Proactive Care, Pavel and his colleagues target technological innovations to support the development of economically feasible, proactive, distributed, and individual-centered health care. In addition, Pavel is investigating approaches to inferring and augmenting human cognition using computer games, EEG, gait characteristics, and transcranial electrical stimulation. Prior to his current positions, he was a program director at the National Science Foundation, faculty at NYU, OHSU, and Stanford University and Member of Technical Staff at Bell Laboratories.

Abstract: The vision of transforming health care from reactive sick care to proactive health care, requires new approaches to the assessment of individuals’ physical, physiological and mental states and their dynamics. Emerging advances in sensing, computation, and communication technology have the potential to enable intensive longitudinal monitoring, assessment, and prediction to close the loop by optimizing early detection and tailored intervention. To reach our goal, we use computational modeling, prediction, and optimization applicable to individuals in specific contexts and scenarios. This presentation will discuss examples of robust computational modeling and predicting individuals’ behaviors combining machine learning, hybrid dynamic systems, and statistical signal processing with psychological knowledge. First we describe inferences of cognitive functionality from computer interactions and games that can be used for early detection of changes in cognitive function. Second, we describe approaches to inferences of stress levels from physiological measurements. Finally, I will describe a principled approach using intensive longitudinal health behavior monitoring to help individuals to increase their physical activity, engagement. We note that an additional benefit of this approach is that it provides transparent explanations of the inferences and recommendations.

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