VISUAL-INERTIAL INTEGRATION FOR HUMAN MOTION TRACKING AND NAVIGATION IN FREE-LIVING ENVIRONMENTS

The aging population has become a burning issue for all modern societies around the world recently. There are two important issues existing now to be solved. One is how to continuously monitor the movements of those people having suffered a stroke in natural living environment for providing more valuable feedback to guide clinical interventions. The other one is how to guide those old people effectively when they are at home or inside other buildings and to make their life easier and convenient. Therefore, human motion tracking and navigation have been active research fields with the increasing number of elderly people. However, motion capture has been extremely challenging to go beyond laboratory environments and obtain accurate measurements of human physical activity especially in free-living environments, and navigation in free-living environments also poses some problems such as the denied GPS signal and the moving objects commonly presented in free-living environments. This thesis seeks to develop new technologies to enable accurate motion tracking and positioning in free-living environments. This thesis comprises three specific goals using our developed IMU board and the camera from the imaging source company: (1) to develop a robust and real-time orientation algorithm using only the measurements from IMU; (2) to develop a robust distance estimation in static free-living environments to estimate people’s position and navigate people in static free-living environments and simultaneously the scale ambiguity problem, usually appearing in the monocular camera tracking, is solved by integrating the data from the visual and inertial sensors; (3) in case of moving objects viewed by the camera existing in free-living environments, to firstly design a robust scene segmentation algorithm and then respectively estimate the motion of the vIMU system and moving objects. To achieve real-time orientation tracking, an Adaptive-Gain Orientation Filter (AGOF) is proposed in this thesis based on the basic theory of deterministic approach and frequency-based approach using only measurements from the newly developed MARG (Magnet, Angular Rate, and Gravity) sensors. To further obtain robust positioning, an adaptive frame-rate vision-aided IMU system is proposed to develop and implement fast vIMU ego-motion estimation algorithms, where the orientation is estimated in real time from MARG sensors in the first step and then used to estimate the position based on the data from visual and inertial sensors. In case of the moving objects viewed by the camera existing in free-living environments, a robust scene segmentation algorithm is firstly proposed to obtain position estimation and simultaneously the 3D motion of moving objects. Finally, corresponding simulations and experiments have been carried out.

UTILIZING DIELECTROPHORESIS TO DETERMINE THE PHYSIOLOGICAL DIFFERENCES OF EUKARYOTIC CELLS

Type 1 diabetes affects over 108,000 children, and this number is steadily increasing. Current insulin therapies help manage the disease but are not a cure. Over a child’s lifetime they can develop kidney disease, blindness, cardiovascular disease and many other issues due to the complications of type 1 diabetes. This autoimmune disease destroys beta cells located in the pancreas, which are used to regulate glucose levels in the body. Because there is no cure and many children are affected by the disease there is a need for alternative therapeutic options that can lead to a cure. Human mesenchymal stem cells (hMSCs) are an important cell source for stem cell therapeutics due to their differentiation capacity, self-renewal, and trophic activity. hMSCs are readily available in the bone marrow, and act as an internal repair system within the body, and they have been shown to differentiate into insulin producing cells. However, after isolation hMSCs are a heterogeneous cell population, which requires secondary processing. To resolve the heterogeneity issue hMSCs are separated using fluorescent- and magnetic-activate cell sorting with antigen labeling. These techniques are efficient but reduce cell viability after separation due to the cell labeling. Therefore, to make hMSCs more readily available for type 1 diabetes therapeutics, they should be separated without diminishing there functional capabilities. Dielectrophoresis is an alternative separation technique that has the capability to separated hMSCs. This dissertation uses dielectrophoresis to characterize the dielectric properties of hMSCs. The goal is to use hMSCs dielectric signature as a separation criteria rather than the antigen labeling implemented with FACS and MACS. DEP has been used to characterize other cell systems, and is a viable separation technique for hMSCs.