Ethan Rodriguez
BME MS Thesis Defense Presentation
Date: 2026-04-15
Time: 1:30 P.M. - 3:30 P.M.
Location / Meeting Link: Physical Location: 57 Executive Park (EP57) Room 109; Teams Link: https://teams.microsoft.com/meet/25733930822466?p=hJDTM7M2VM6JkqWFx2

Committee Members:
Madeline Hackney; Rudolph Gleason; Zachary Danzinger; Johnathan McKay


Title: Biomechanical Feature Evaluation for Movement‑Quality Classification in Robot‑Facilitated Exercise for Parkinson’s Disease 

Abstract:
Parkinson’s disease (PD) is characterized by motor impairments that significantly affect movement quality, which makes accurate assessment of human motion imperative for rehabilitation efforts and long-term monitoring. Targeted exercise is a commonly prescribed and clinically effective treatment for managing PD symptoms and slowing functional decline. Current therapeutic exercise apparatuses have continually and increasingly relied on robotic assistance to chaperone movement, provide feedback, and capture biomechanical data through human pose estimates and wearable sensors. However, it is unclear which of these biomechanical features within the set of robot-derived human pose estimate signals best correlate to the quality of movement. This thesis presents an interpretable human kinematic model that converts IMU-derived joint angles into Cartesian coordinate positions of major human body parts. Using expert labels of exercise form provided by physical therapists, we evaluate single-feature logistic regression models, leveraging each biomechanical feature to classify correct or incorrect exercise form. The results demonstrate that the time series features of Cartesian joint positions are sufficient inputs to single-dimensional logistic regression models for exercise form classification. This work provides an interpretable, feature-prioritized methodology for understanding the quality of movement assessment in PD.