Sensing and Analysis Systems for Parkinson's Disease Management: A Survey

PhD Qualifying Examination


Title: "Sensing and Analysis Systems for Parkinson's Disease Management: A 
Survey"

by

Miss Yizhen ZHANG


Abstract:

Parkinson's Disease (PD) is the world's second most prevalent 
neurodegenerative disorder, traditionally managed through the 
semi-quantitative MDS-UPDRS scale. However, this approach is limited by 
subjective clinician bias and a "snapshot" nature that fails to capture daily 
motor fluctuations. Consequently, PD management is undergoing a paradigm 
shift toward continuous, objective, and home-based digital monitoring, driven 
by the need to capture "real-world performance" rather than just "clinical 
capacity."

This survey systematically reviews the landscape of PD sensing and analysis 
systems through a three-tier hierarchical architecture: Perception, 
Processing, and Output. We first analyze the Perception layer, categorizing 
multi-modal data sources into contact-based wearables, non-contact ambient 
sensing (e.g., Vision and RF), and behavioral digital interactions. 
Furthermore, we discuss the evolution of the Processing layer, where raw 
heterogeneous data is distilled into refined digital biomarkers using 
paradigms ranging from traditional kinematic modeling to AI-driven deep 
learning. The Output layer investigates the mapping of these parameters to 
actionable clinical insights and quantified MDS-UPDRS scores. Specifically, 
we explore the transition of application scenarios from task-driven clinical 
assessments to passive longitudinal symptom tracking.

To address the critical research gaps in self-administered home testing and 
high-fidelity motion reconstruction, we highlight two of our research 
projects: PIGDAssess, which utilizes dual-task interference for autonomous 
postural stability evaluation, and TulipTender, a physics-aware 3D motion 
analysis framework using monocular commodity cameras. Finally, we identify 
future directions, including closed-loop management and the integration of 
Large Language Models (LLMs) for clinical decision support. This review 
underscores the transformative impact of deep learning-driven sensing in 
shifting PD care toward a proactive, precise, and intelligent management 
paradigm.


Date:                   Monday, 20 April 2026

Time:                   4:00pm - 6:00pm

Venue:                  Room 2126A
                        Lift 19

Committee Members:      Prof. Qian Zhang (Supervisor)
                        Prof. Mo Li (Chairperson)
                        Dr. Xiaomin Ouyang