Publication
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.