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With an increase in age comes an increase in the risk of frailty and mobility decline, which can lead to dangerous falls and can even be a cause of mortality. Despite these serious consequences, healthcare systems remain reactive, highlighting the need for technologies to predict functional mobility decline. In this thesis, we present an end-to-end autonomous functional mobility assessment system that seeks to bridge the gap between robotics research and clinical rehabilitation practices. Unlike many fully integrated black-box models, our approach emphasizes the need for a system that is both reliable as well as transparent to facilitate its endorsement and adoption by healthcare professionals and patients. Our proposed system is characterized by the sensor fusion of multimodal data using an optimization framework known as factor graphs. This method, widely used in robotics, enables us to obtain visually interpretable 3-D estimations of the human body in recorded footage. These representations are then used to implement autonomous versions of standardized assessments employed by physical therapists for measuring lower-limb mobility, using a combination of custom neural networks and explainable models. To improve the accuracy of the estimations, we investigate the application of the Koopman operator framework to learn linear representations of human dynamics: We leverage these outputs as prior information to enhance the temporal consistency across entire movement sequences. Furthermore, inspired by the inherent stability of natural human movement, we propose ways to impose stability constraints in the dynamics during the training of linear Koopman models. In this light, we propose a sufficient condition for the stability of discrete-time linear systems that can be represented as a set of convex constraints. Additionally, we demonstrate how it can be seamlessly integrated into larger-scale gradient descent optimization methods. Lastly, we report the performance of our human pose detection and autonomous mobility assessment systems by evaluating them on outcome mobility datasets collected from controlled laboratory settings and unconstrained real-life home environments. While we acknowledge that further research is still needed, the study results indicate that the system can demonstrate promising performance in assessing mobility in home environments. These findings underscore the significant potential of this and similar technologies to revolutionize physical therapy practices.

The electrical stimulation of appropriate components of the human visual system can result in the perception of blobs of light (or phosphenes) in totally blind patients. By stimulating an array of closely aligned electrodes it is possible for a patient to perceive very low-resolution images from spatially aligned phosphenes. Using this approach, a number of international research groups are working toward developing multiple electrode systems (called Artificial Human Vision (AHV) systems or visual prostheses) to provide a phosphene-based substitute for normal human vision. Despite the great promise, there are currently a number of constraints with current AHV systems. These include limitations in the number of electrodes which can be implanted and the perceived spatial layout and display frequency of phosphenes. Therefore the development of computer vision techniques that can maximise the visualisation value of the limited number of phosphenes would be useful in compensating for these constraints. The lack of an objective method for comparing different AHV system displays, in addition to comparing AHV systems and other blind mobility aids (such as the long cane), has been a significant problem for AHV researchers. Finally, AHV research in Australia and many other countries relies strongly on theoretical models and animal experimentation due to the difficult of prototype human trials. Because of this constraint the experiments conducted in this thesis were limited to simulated AHV devices with normally sighted research participants and the true impact on blind people can only be regarded as approximated. In light of these constraints, this thesis has two general aims. The first aim is to investigate, evaluate and develop effective techniques for mobility assessment which will allow the objective comparison of different AHV system phosphene presentation methods. The second aim is to develop a useful display framework to guide the development of AHV information presentation, and use this framework to guide the development of an AHV simulation device. The first research contribution resulting from this work is a conceptual framework based on literature reviews of blind and low vision mobility, AHV technology, and computer vision. This framework incorporates a comprehensive number of factors which affect the effectiveness of information presentation in an AHV system. Experiments reported in this thesis have investigated a number of these factors using simulated AHV with human participants. It has been found that higher spatial resolution is associated with accurate walking (reduced veering), whereas higher display rate is associated with faster walking speeds. In this way it has been demonstrated that the conceptual framework supports and guides the development of an adaptive AHV system, with the dynamic adjustment of display properties in real-time. The second research contribution addresses mobility assessment which has been identified as an important issue in the AHV literature. This thesis presents the adaptation of a mobility assessment method from the blind and low vision literature to measure simulated AHV mobility performance using real-time computer based analysis. This method of mobility assessment (based on parameters for walking speed, obstacle contacts and veering) is demonstrated experimentally in two different indoor mobility courses. These experiments involved sixty-five participants wearing a head-mounted simulation device. The final research contribution in this thesis is the development and evaluation of an original real-time looming obstacle detector, based on coarse optical flow, and implemented on a Windows PocketPC based Personal Digital Assistant (PDA) using a CF card camera. PDA based processors are a preferred main processing platform for AHV systems due to their small size, light weight and ease of software development. However, PDA devices are currently constrained by restricted random access memory, lack of a floating point unit and slow internal bus speeds. Therefore any real-time software needs to maximise the use of integer calculations and minimise memory usage. This contribution was significant as the resulting device provided a selection of experimental results and subjective opinions.