A Modular Architecture for Adaptive and Personalized Cognitive Prosthetics

Presenter: Candace Williams

Faculty Sponsor: Poonam Kumari

School: Bridgewater State University

Research Area: Computer Science

Session: Poster Session 3, 1:15 PM - 2:00 PM, 163, C8

ABSTRACT

Personalization of assistive technologies is critical for adaptive cognitive support. However, current tools often rely on systems that lack closed-loop personalization and use strategies that focus on the characteristics of a general population, not each individual person. One method for the system to adjust itself to each person is by using neural signatures in tandem with deep learning, something that can result in a unique system for each human being. The primary goal of this research is to create a modular closed-loop architecture for personalized cognitive augmentation and rehabilitation using multimodal physiological signals and latent state modeling. This architecture combines electroencephalogram and electrocardiogram signals into a vector which represents the cognitive state of the individual at a given point in time. Knowledge distillation is then used to create a smaller model that can be deployed on a wearable device. The representation of cognitive state is used to create a reinforcement learning policy that an adaptive mechanism uses to adjust suggested health interventions. This is formally specified as a pipeline encompassing the following modules: fusion, distillation, calibration, and adaptation, with mathematical formalization and data flows. Thus, the proposed architecture will provide a personalization approach using latent cognitive state as a control variable and a deployable wearable strategy.

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