无需标记点，自动分割并分析步行测试的各个阶段。
Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline
- 基于空间阈值自动划分站立、行走、转身等五个阶段。
- 通过相对距离法精准检测足跟触地与离地事件。
- 输出包括动态稳定性指标，适合临床与科研使用。
可穿戴式定时起立行走（TUG）分析能辅助临床与研究决策，但鲁棒且可重复的无标记点流程仍有限。本文提出 extit{tugturn.py}，一个基于 Python 的 3D 无标记点 TUG 处理工作流，整合了阶段分割、步态事件检测、时空指标、节段协调性及动态稳定性分析。该流程利用空间阈值将每次试验划分为站立、首次行走、转身、第二次行走和坐下五个阶段，并在有效步态窗口内采用相对距离策略检测足跟触地与足尖离地事件。除常规运动学参数外， extit{tugturn} 还提供向量编码输出及基于拓展质心（XCoM）的指标。软件通过 TOML 文件配置，生成可复现的结果，包括 HTML 报告、CSV 表格和质量控制可视化输出。完整可运行示例附带测试数据与命令行说明。本文聚焦于 extit{tugturn} 的实现、输出与可复现性工作流，作为无标记点生物力学 TUG 分析的软件贡献。
原文摘要 · Abstract (English)
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.
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