ORCID

https://orcid.org/0009-0002-5112-0849

Date of Award

Summer 2025

Language

English

Embargo Period

7-30-2025

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College/School/Department

Department of Computer Science

Program

Computer Science

First Advisor

Thenkurussi Kesavadas

Second Advisor

Aishwari Talhan

Third Advisor

Balakrishnan Prabhakaran

Committee Members

Thenkurussi Kesavadas, Aishwari Talhan, Balakrishnan Prabhakaran

Keywords

Extended Reality, Stroke, Rehabilitation, Mixed Reality, Immersive, Lower Limb Rehabilitation

Subject Categories

Computer and Systems Architecture | Other Computer Engineering

Abstract

This thesis presents the design, deployment, and pilot study of an immersive extended-reality (XR) rehabilitation system integrated with a ceiling-mounted dynamic body-weight support device (Vector Gait and Safety System), aimed at improving lower-limb rehabilitation out- comes. The implemented system combined immersive virtual tasks—such as Touch Wall, Ball Launcher, Obstacle Dodge, and Stepping Stones—with real-time movement tracking, enabling detailed kinematic analysis and personalized therapy. A pilot study conducted at Sunnyview Rehabilitation Hospital involved seven patients with various mobility impairments, providing quantitative performance metrics and quali- tative user feedback. Results demonstrated consistent patient engagement, measurable im- provements in gait speed and task efficiency over repeated sessions, as well as emerging adaptive movement strategies. Patients reported high motivation, comfort, and satisfaction with the XR-based tasks, supported by positive therapist observations highlighting enhanced patient autonomy and reduced self-consciousness during rehabilitation. The thesis concludes by discussing how this work fits within the broader landscape of extended reality (XR) and artificial intelligence (AI) in rehabilitation. The discussion section specifically examines how the findings relate to general trends in XR and AI-based rehabil- itation, highlighting both the opportunities these technologies present and the challenges they pose. Future work will focus on integrating real-time AI-driven adaptation, natural language interfaces, and expanded rehabilitation task libraries, with particular emphasis on accommodating diverse neurological populations, including those recovering from traumatic brain injury (TBI). Overall, this research contributes significantly to the advancement of intelligent, adaptive, and patient-centered XR rehabilitation solutions, offering substantial promise for transforming clinical practice and enhancing patient recovery outcomes.

License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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