Data Sheet 1_Toward a unified gait freeze index: a standardized benchmark for clinical and regulatory evaluations.pdf

Freezing of Gait (FOG) is a disabling motor symptom that affects a majority of individuals with advanced Parkinson's disease, severely limiting mobility, independence, and quality of life. Automatic methods for detecting FOG using the freeze index (FI) have been widely proposed to systematically monitor FOG in real life and guide therapy optimizations. However, methods to estimate the FI have relied on a broad range of measurement technologies and computational methodologies, often lacking mathematical rigor. The inconsistency across studies has made it difficult to directly compare results or draw definitive conclusions. This lack of standardization has severely hindered the acceptance of FI by regulatory agencies as a reproducible, robust, effective and safe measure on which to base further developments. In this study, we formalize the definition of the FI and propose a rigorous, explicit estimation algorithm, which may serve as a standard for future applications. This standardization provides a consistent and reliable benchmark. We also provide an overview of existing FI estimation methods, discuss their limitations, and compare each one of them with the proposed standard. Our method demonstrates improved performance compared to existing approaches while effectively mitigating the risk of divergent outcomes, which could otherwise lead to unforeseen and potentially hazardous consequences in real-world applications. Our algorithm is made available as open-source Python code, promoting accessibility and reproducibility.

步态冻结（Freezing of Gait, FOG）是一种致残性运动症状，累及多数晚期帕金森病患者，严重损害其运动能力、独立性与生活质量。此前已有诸多基于冻结指数（Freeze Index, FI）的自动检测方法被提出，用于系统化监测日常场景下的FOG并指导治疗方案优化。然而，现有冻结指数的估算方法依赖多样的测量技术与计算手段，往往缺乏严谨的数学推导。不同研究间的方法不一致性，使得直接比对研究结果或得出确定性结论变得困难。这种标准化缺失严重阻碍了监管机构认可冻结指数作为可重复、稳健、有效且安全的量化指标，进而支撑后续相关开发工作。本研究明确了冻结指数的定义，并提出一套严谨且明晰的估算算法，可为未来相关应用提供标准范式。该标准化方案可提供一致且可靠的基准。此外，本研究还梳理了现有冻结指数估算方法，讨论其局限性，并将各类方法与所提出的标准进行对比。本算法相较于现有方案展现出更优的性能，同时有效规避了结果发散的风险——此类发散若未被控制，可能在实际应用中引发不可预见的潜在不良后果。本算法以开源Python代码形式公开，以提升其可及性与可重复性。