Computer Vision Based Rehabilitation and Motor Function Assessment with Clinical Study and Validation for Upper Limb Recovery in Post Stroke Patients

Abstract

Stroke-induced upper limb motor deficits remain one of the most prevalent and debilitating outcomes among survivors, significantly impairing daily functioning and quality of life. Traditional rehabilitation practices, while clinically effective, are often constrained by their dependence on in-person evaluations, the subjectivity of observational assessments, and limited accessibility for individuals facing mobility or logistical barriers. These challenges underscore the growing necessity for remote, objective, and scalable rehabilitation and evaluation tools that can support both patients and clinicians. In response to this pressing need, this Thesis explores the integration of computer vision technologies into post-stroke upper limb rehabilitation frameworks, aiming to deliver accurate, real-time motor performance assessments without the requirement for specialized sensors or equipment. The work presented herein introduces both desktop-based and mobile-oriented systems that leverage standard cameras and modern computer vision techniques to simulate established rehabilitation protocols, such as the Box and Block Test (BBT) and the Sollerman Hand Function Test (SHFT). Through algorithmic tracking of hand movements and gesture recognition, the proposed applications automatically compute clinically relevant metrics in a non-invasive, user-friendly manner. The Thesis outlines the complete software architecture of these systems, including interaction modeling, gesture interpretation, and augmented feedback mechanisms. In addition to providing an immersive and engaging rehabilitation experience, both implementations were rigorously performance-evaluated with respect to latency, robustness, and system responsiveness, under a variety of computational and environmental conditions. These evaluations confirm the technical feasibility of delivering high-fidelity rehabilitation tools across both platforms, enhancing the accessibility and practicality of home-based motor recovery interventions. To support clinical relevance and interpretability of the measured outcomes, a comprehensive baseline score normalization study was conducted using a cohort of healthy individuals to establish normative performance ranges. This process enables direct comparison between users and facilitates the detection of motor impairments with greater precision. Furthermore, a clinical validation study was carried out involving older post-stroke individuals, wherein the system’s outputs were compared against conventional therapist-administered evaluations. Results indicate strong concordance between automated and manual assessments, demonstrating the potential of computer vision-driven tools to augment clinical decision-making and empower patients to engage in effective, self-directed rehabilitation. Overall, this Thesis offers a robust, scalable, and clinically grounded framework for enhancing upper limb rehabilitation through accessible computer vision methodologies. The Thesis is organized into Sections. Following the Introduction (Section 1), Section 2 presents the computer vision foundations underpinning the system, including key algorithms, architectural choices, and real-time implementation strategies. It also examines augmented reality as an interactive medium for motor rehabilitation, discussing relevant design principles, healthcare use cases, and technical constraints. Furthermore, the section situates the work within established clinical rehabilitation standards by detailing upper limb rehabilitation protocols and motor assessments such as the BBT and the SHFT. Lastly, it reviews existing digital adaptations of these assessments, identifying limitations in current approaches and outlining integration challenges that this Thesis seeks to address. Section 3 describes the desktop-based system architecture, including virtual test modules and a custom exergame to enhance engagement. Section 4 extends the system to mobile platforms, highlighting design adaptations and deployment strategies for at-home use. Section 5 provides a comparative evaluation of technical performance metrics across platforms under varied conditions. Section 6 presents clinical validation via score normalization and trials with post-stroke individuals, assessing diagnostic and therapeutic outcomes. Finally, Section 7 summarizes the contributions and proposes directions for future research and system expansion.