Robotic Outcome Assessment in a Non-Human Primate Model of Middle Cerebral Artery Stroke

Stroke is a prevalent disorder with immense socioeconomic impact. A variety of chronic neurological deficits result from stroke. In particular, sensorimotor deficits are a significant barrier to achieving post-stroke independence. Unfortunately, the majority of pre-clinical studies that show improved outcomes in animal stroke models have failed in clinical trials. Pre-clinical studies using non-human primate (NHP) stroke models prior to initiating human trials are a potential step to improving translation from animal studies to clinical trials. Robotic assessment tools represent a quantitative, reliable, and reproducible means to assess reaching behaviour following stroke in both humans and NHPs. We investigated the use of robotic technology to assess sensorimotor impairments in NHPs following middle cerebral artery occlusion (MCAO). Two cynomolgus macaques underwent transient MCAO for 90 minutes. Approximately 1.5 years following the procedure these NHPs and two non-stroke control monkeys were trained in a reaching task with both arms in the KINARM exoskeleton. This robot permits elbow and shoulder movements in the horizontal plane. The task required NHPs to make reaching movements from a centrally positioned start target to 1 of 8 peripheral targets uniformly distributed around the first target. We analyzed four movement parameters: reaction time, movement time (MT), initial direction error (IDE), and number of speed maxima to characterize sensorimotor deficiencies. We hypothesized reduced performance in these attributes during a neurobehavioural task with the paretic limb of NHPs following MCAO compared to controls. Reaching movements in the non-affected limbs of control and experimental NHPs showed bell-shaped velocity profiles. In contrast, the reaching movements with the affected limbs were highly variable. We found distinctive patterns in MT, IDE, and number of speed peaks between control and experimental monkeys and between limbs of NHPs with MCAO. NHPs with MCAO demonstrated more speed peaks, longer MTs, and greater IDE in their paretic limb compared to controls. These initial results qualitatively match human stroke subjects’ performance, suggesting that robotic neurobehavioural assessment in NHPs with stroke is feasible and could have translational relevance in subsequent human studies. Further studies will be necessary to replicate and expand on these preliminary findings.

Distributed Extremum-Seeking Control with Applications to High-Altitude Balloons

The real-time optimization of large-scale systems is a difficult problem due to the need for complex models involving uncertain parameters and the high computational cost of solving such problems by a decentralized approach. Extremum-seeking control (ESC) is a model-free real-time optimization technique which can estimate unknown parameters and can optimize nonlinear time-varying systems using only a measurement of the cost function to be minimized. In this thesis, we develop a distributed version of extremum-seeking control which allows large-scale systems to be optimized without models and with minimal computing power. First, we develop a continuous-time distributed extremum-seeking controller. It has three main components: consensus, parameter estimation, and optimization. The consensus provides each local controller with an estimate of the cost to be minimized, allowing them to coordinate their actions. Using this cost estimate, parameters for a local input-output model are estimated, and the cost is minimized by following a gradient descent based on the estimate of the gradient. Next, a similar distributed extremum-seeking controller is developed in discrete-time. Finally, we consider an interesting application of distributed ESC: formation control of high-altitude balloons for high-speed wireless internet. These balloons must be steered into a favourable formation where they are spread out over the Earth and provide coverage to the entire planet. Distributed ESC is applied to this problem, and is shown to be effective for a system of 1200 ballons subjected to realistic wind currents. The approach does not require a wind model and uses a cost function based on a Voronoi partition of the sphere. Distributed ESC is able to steer balloons from a few initial launch sites into a formation which provides coverage to the entire Earth and can maintain a similar formation as the balloons move with the wind around the Earth.