Mobile vision-based kinematic screening for ballet: A pilot validation of a contactless approach using geometric constraints

High-fidelity biomechanical assessment typically relies on instrumented systems (e.g. optical motion capture or inertial measurement units (IMUs)), which can be costly, intrusive, and difficult to deploy at scale. This study presents a contactless, smartphone-based framework for preliminary kinematic screening of a ballet Grand Plié task using monocular video. Using MediaPipe BlazePose, the system derives 2D pose-vector indicators for knee-flexion and alignment-related features and applies dynamic time warping (DTW) to compare input sequences with reference template trajectories derived from the training set after temporal alignment and averaging. A retrospective dataset of 250 videos was curated from public sources; after quality control and segmentation, 220 valid motion instances (video clips) were retained for analysis. On a contemporary mobile device (Snapdragon 8 Gen 1), the pipeline runs in real time, achieving 22 frames per second (FPS). Agreement with an expert-derived 2D reference (frame-based digital goniometry; n = 50 key frames) yielded a mean bias of +1.2° and 95% limits of agreement (LoA) of −3.3° to +5.6°. A Monte Carlo sensitivity analysis indicated resilience to simulated landmark perturbations (additive Gaussian noise with σ = 0.01 applied to normalized coordinates), resulting in a mean angular error of approximately 4.0°. In a subject-level pilot evaluation ( N = 5), the framework achieved an F 1-score of 0.921 for detecting gross form deviations under the specified capture protocol. Overall, these results support the feasibility of mobile, markerless video-based screening as a practical complement to instrumented measurement for preliminary assessment under resource constraints.