Computational modeling of cognitive processes for continuous in-home assessment of cognitive performance

A rapidly growing proportion of older adults in the populations of the US, EU, and Japan, combined with an increasing prevalence of conditions associated with aging, and further exacerbated by behavioral issues, comprise significant challenges to healthcare delivery. The development of cost-effective, proactive, and preventive approaches, focused on quality-of-life is therefore of utmost societal importance. Replacing institution-centered (clinic-centered) reactive approaches with user-centered sensor and computer-aided care is emerging as a potential solution that would allow early detection and intervention through continuous monitoring and assessment. In contrast to well controlled, in-clinic measurements, however, the context of the user-centered behavioral observations is typically unknown, and the quantities measured by the sensors are usually remote from the quantities of interest. These aspects pose significant challenges for the development of robust algorithms. The only way to mitigate these impediments is the development of computational models that relate the observable quantities to meaningful behavioral metrics. The focus of this thesis is the development of several algorithmic techniques based on computational models of behaviors.