Rashida Nayeem’s PhD Dissertation Defense

“Human control of objects with nonlinear internal dynamics: Predictability as primary objective”

Location:
Egan Research Ctr 206

Committee Members:
Prof. Dagmar Sternad (Advisor)
Prof. Eduardo Sontag
Prof. Mario Sznaier
Dr. David Lin (Massachusetts General Hospital & Harvard Medical School)

Abstract:
Humans physically interact with complex objects in numerous daily activities. An example is picking up a cup of coffee where interaction forces arise between the hand and the sloshing liquid. For successful actions, error corrections based on real-time sensed information are insufficient, hence humans need to predict and preempt the evolving dynamics. Our previous work on the transport of a “cup of coffee” showed that humans seek to make the interaction dynamics simple, i.e., predictable. Extending from previous work, this thesis used a virtual paradigm where the “cup of coffee” was simplified to a cup with a ball sliding inside, retaining the challenges of “a cup filled with coffee”: underactuation and nonlinearity. A series of experiments examined human strategies in different contexts to demonstrate that predictability is a control priority. The first experimental and modeling study examined how subjects explored and prepared the 2D cup-and-ball system prior to continuous interaction. Results showed that subjects converged to a small set of initial conditions that shortened initial transients, enabling subjects to reach a more predictable steady state faster. Two follow-up studies examined the role of visual and haptic information and revealed that despite suboptimal exploration of the solution space, subjects increased predictability of hand object interactions. System identification showed that visual information enabled subjects to simplify input-output behavior via appropriate object preparation. When deprived of haptic information subjects still achieved increased predictability but sacrificed orbital stability. A final study extended this basic paradigm to a clinical application to investigate if these insights could help in assessment of motor impairment after stroke in this functionally relevant ‘self-feeding’ task. To facilitate testing in a clinic, a real-life 3D device was custom-developed where individuals after stroke moved a cup with a rolling ball inside on a table. Our theory-based predictability metric proved highly sensitive to quantify the degree of motor impairment after stroke. Taken together, this thesis elucidated principles of human motor control in a complex interactive task. The insights have significant applications in clinical testing and may also inform robot manipulation of this understudied movement challenge.