Parkinson's disease is characterized by bradykinesia and impaired motor scaling, leading to reduced movement amplitude and diminished functional performance. Large-amplitude exercise approaches are widely used to counteract these deficits by promoting exaggerated movement execution and recalibration of internal movement perception. However, during repetitive exercise sets, individuals often demonstrate a gradual reduction in movement amplitude, a phenomenon known as amplitude decay, which typically requires verbal correction by a clinician.
Despite its clinical relevance, amplitude decay has not been objectively quantified, and movement quality is commonly assessed through visual observation alone. Marker-based motion capture systems, while accurate, are impractical for routine or remote use. RGB camera-based markerless analysis offers a low-cost and accessible alternative for objective movement assessment, particularly in telerehabilitation contexts where direct visual supervision is limited.
Participants and Study Design
A total of 22-26 healthy volunteers aged 18-45 years will be recruited. All participants will be capable of independent functional movement and will provide written informed consent prior to participation. The study follows a single-session, observational design.
Exercise Protocol
Participants will perform six standardized large-amplitude exercises:
Floor-to-Ceiling Reach
Forward Step
Side Step
Sit-to-Stand
Forward Reach to Grasp and Release an Object
Big Walking
Each exercise will be recorded for eight repetitions using a single RGB camera capturing full-body movement. No wearable sensors or reflective markers will be used.
For each exercise, the first repetition will be performed without prior instruction regarding maximal effort. Immediately after completion, a clinician expert will verbally confirm whether this execution represents the participant's maximum movement amplitude. Upon confirmation, this repetition will be designated as the individual reference trial for that exercise.
Data Processing and Outcome Measures
Movement data will be analyzed using two complementary approaches:
Pose-Based Analysis:
Joint keypoints will be extracted from video using a pose estimation algorithm. Movement amplitude will be computed using joint displacement-based metrics and normalized according to individual anthropometric characteristics. Amplitude decay will be quantified by comparing early and late repetitions and by calculating repetition-wise trends.
Visual Representation Analysis:
As an exploratory component, a self-supervised vision transformer will be used to extract visual feature representations from exercise videos. Each repetition will be compared to the clinician-confirmed reference repetition using feature similarity measures. Repetition-wise similarity scores will be used to characterize within-exercise changes and identify patterns consistent with amplitude decay.
Statistical Analysis
Data distribution will be assessed for normality. Changes in movement amplitude and repetition-wise trends will be analyzed using paired statistical methods. Associations between camera-based measures and clinician observations will be explored using correlation analysis, with statistical significance set at p < 0.05.
Ethical Considerations and Expected Impact
The study involves no invasive procedures or therapeutic interventions. Video data will be anonymized and securely stored, and participants may withdraw at any time. Ethical approval has been obtained from the Biruni University Non-Interventional Ethics Committee.
This study is expected to demonstrate the feasibility of camera-based methods for objective assessment of movement amplitude and amplitude decay, providing a methodological foundation for future studies involving patient populations, automated feedback systems, and telerehabilitation applications
