• Starts: 11:00 am on Friday, June 7, 2024
  • Ends: 1:00 pm on Friday, June 7, 2024

ABSTRACT: 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 the 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 utilized to autonomously evaluate 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 learning 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 explore imposing stability 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 demonstrates promising performance in assessing mobility in home environments. These findings underscore the significant potential of this and similar technologies to revolutionize physical therapy practices.

COMMITTEE: ADVISOR Professor Roberto Tron, ME/SE; CHAIR Professor Scott Bunch, ME/MSE; Professor Sean Andersson, ME/SE; Professor Alyssa Pierson, ME/SE; Professor Lou Awad, ME

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