For over half a century, computer scientists and psychologists have strived to build machines that teach humans automatically, sometimes dubbed intelligent tutoring systems (ITS). The earliest such systems focused on "flashcard"-style vocabulary learning, while more modern ITS can tutor students in diverse subjects such as high school geometry, physics, algebra, and computer programming. Compared to human tutors, however, most contemporary ITS still use a rather impoverished set of low-bandwidth sensors consisting of mouse clicks, keyboard strokes, and touch events. In contrast, human teachers utilize not only students' explicit responses to practice problems and test questions, but also auditory and visual information about the students' affective, or emotional, states, to make decisions. It is possible that, if automated teaching systems were affect-sensitive and could reliably detect and respond to their students' emotions, then they could teach even more effectively. In this dissertation we examine the affect-sensitive teaching problem from a stochastic optimal control (SOC) perspective. Stochastic optimal control theory provides a rigorous computational framework for describing the challenges and possible benefits of affect-sensitive teaching systems, and also provides computational tools that may help in building them. After framing the problem of affect-sensitive teaching using the language of SOC, we (1) present an experimental technique for measuring the importance to teaching of affect-sensitivity within a given learning domain. Next, we develop machine learning and computer vision tools to recognize automatically certain aspects of the student's affective state in real- time, including (2) student "engagement" and (3) the student's perception of curriculum difficulty. Finally, (4) we propose and evaluate an automated procedure, based on SOC, for creating an automated teacher that teaches foreign language by image association (a la Rosetta Stone). In a language learning experiment on 90 human subjects, the controller developed using SOC showed higher learning gains compared to two heuristic controllers, and also allows for affective observations to be easily integrated into the decision-making process

Objective

Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures. The variability could be circumvented with methods to measure CD head posture objectively. However, previously used objective methods require specialized equipment and have been limited to studies with a small number of cases. The objective of this study was to evaluate a novel software system-the Computational Motor Objective Rater (CMOR)-to quantify multi-axis directionality and severity of head posture in CD using only conventional video camera recordings.

Methods

CMOR is based on computer vision and machine learning technology that captures 3D head angle from video. We used CMOR to quantify the axial patterns and severity of predominant head posture in a retrospective, cross-sectional study of 185 patients with isolated CD recruited from 10 sites in the Dystonia Coalition.

Results

The predominant head posture involved more than one axis in 80.5% of patients and all three axes in 44.4%. CMOR's metrics for head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS-2 and an adapted version of the Global Dystonia Rating Scale (rho = 0.59-0.68, all p <0.001).

Conclusions

CMOR's convergent validity with clinical rating scales and reliance upon only conventional video recordings supports its future potential for large scale multisite clinical trials.