Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline

Abel Gonçalves Chinaglia, Guilherme Manna Cesar, Paulo Roberto Pereira Santiago

TL;DR

A Python-based workflow for 3D markerless TUG processing that combines phase segmentation, gait-event detection, spatiotemporal metrics, intersegmental coordination, and dynamic stability analysis is presented.

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.

Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline

TL;DR

A Python-based workflow for 3D markerless TUG processing that combines phase segmentation, gait-event detection, spatiotemporal metrics, intersegmental coordination, and dynamic stability analysis is presented.

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.
Paper Structure (11 sections, 2 figures, 2 tables)

This paper contains 11 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Software Architecture and Methodology
  3. Spatial Phase Segmentation (The Y-Axis Logic)
  4. Gait Event Detection (Dynamic Zeni Algorithm)
  5. Advanced Kinematics and Intersegmental Coordination
  6. Implementation and Usage (High-Throughput Architecture)
  7. Results-Oriented Software Deliverables
  8. Example Run Output (HTML Reports)
  9. Discussion
  10. Conclusion
  11. Example Run Reports in HTML

Figures (2)

  • Figure 1: The vailá GUI, highlighting the tugturn button used to initialize the analysis module.
  • Figure 2: High-level tugturn.py processing flow used in this study.