🤖 AI Summary
Current markerless Timed Up and Go (TUG) analyses lack robustness and reproducibility, limiting their utility in clinical and research settings. This work proposes tugturn.py, a Python-based, end-to-end markerless 3D TUG analysis pipeline that, for the first time, integrates phase segmentation, gait event detection, and advanced biomechanical metrics—such as Vector Coding and extrapolated Center of Mass (XCoM)—within a unified framework. The method employs spatial thresholds for phase segmentation and a relative distance strategy to identify heel-strike and toe-off events, leveraging 3D pose estimation to generate HTML reports, CSV outputs, and quality-control visualizations. Full reproducibility is ensured through TOML configuration files, while a command-line interface and comprehensive examples enhance accessibility. This pipeline substantially advances the standardization, reliability, and practical applicability of markerless TUG assessment.
📝 Abstract
Instrumented Timed Up and Go (TUG) analysis can support clinical and research decision-making, but robust and reproducible markerless pipelines are still limited. We present \textit{tugturn.py}, a Python-based workflow for 3D markerless TUG processing that combines phase segmentation, gait-event detection, spatiotemporal metrics, intersegmental coordination, and dynamic stability analysis. The pipeline uses spatial thresholds to segment each trial into stand, first gait, turning, second gait, and sit phases, and applies a relative-distance strategy to detect heel-strike and toe-off events within valid gait windows. In addition to conventional kinematics, \textit{tugturn} provides Vector Coding outputs and Extrapolated Center of Mass (XCoM)-based metrics. The software is configured through TOML files and produces reproducible artifacts, including HTML reports, CSV tables, and quality-assurance visual outputs. A complete runnable example is provided with test data and command-line instructions. This manuscript describes the implementation, outputs, and reproducibility workflow of \textit{tugturn} as a focused software contribution for markerless biomechanical TUG analysis.