Intelligent Treadmill Control and Holographic Rendering for Accessible Rehabilitation

There is a growing need in the medical rehabilitation market due to the increasing elderly population. More than half of the patients are outpatients who require commuting from home to nursing facilities. However, the geographic distribution of these facilities is highly imbalanced, with states like Texas and California housing the largest number, while many rural and remote areas face a shortage. This disparity creates a significant burden for patients who require continuous rehabilitation but struggle with long commutes. Addressing this gap in rehabilitation accessibility is the central focus of this dissertation. This work approaches the problem from two complementary directions: (1) reducing the cost of home-based rehabilitation equipment, and (2) advancing telerehabilitation technologies. The first part of the dissertation introduces an intelligent treadmill control system that enables a single-belt treadmill to function like a split-belt treadmill, thereby providing a cost-effective solution for post-stroke gait rehabilitation at home. This system integrates real-time gait classification models and adaptive speed control to simulate split-belt dynamics without requiring specialized hardware. The second part explores novel telerehabilitation solutions. We adopted advanced neural rendering techniques to build a low-cost 3D patient reconstruction pipeline to enhance remote patient monitoring and engagement. Through these innovations, this dissertation contributes to making rehabilitation more accessible, affordable, and effective, particularly for patients in underserved areas. The proposed solutions aim to bridge the gap between clinical rehabilitation and home-based care, ultimately improving patient outcomes and reducing healthcare disparities.