Freezing Prediction Horizon: Quantifying Advanced Warning for Predicting Freezing of Gait in Parkinson's Disease

Freezing of gait (FoG) prediction is clinically meaningful only when warnings arrive sufficiently early for subsequent action. Therefore, we adopt a Freezing Prediction Horizon (FPH) evaluation that reports prediction performance as a function of the warning horizon before onset, making the lead-time versus reliability trade-off explicit. Within this protocol, we develop a Transformer-based predictor with a progressive self-paced learning strategy and evaluate it on a 55-patient clinical dataset and two public datasets. The horizon-performance curves show that Macro-F1 remains stable up to approximately 2.5 seconds before FoG onset in our dataset, after which a gradual decline is observed. This horizon-based characterization replaces single, fixed ahead-of-onset windows with a continuous method that summarizes achievable advanced time at specified accuracy levels. In this way, it offers a principled basis for setting targets in real-time implementationslinking algorithmic early-warning capacity to the lead times that practical systems may require-while remaining compatible with conventional metrics. By centering evaluation on FPH, this study clarifies how far in advance FoG can be predicted with confidence, and it positions horizon-based assessment as a reproducible complement to standard reporting for future work on deployable FoG prediction. Ultimately, quantifying advance warning is a prerequisite for prevention-oriented use, by indicating whether sufficient time can be reserved for cueing prior to onset.