Hierarchical Coarse-to-Fine cGAN for Subtype-Specific Freezing of Gait Signal Generation

Abstract

Freezing of gait (FOG), a debilitating symptom of Parkinsons disease, manifests in subtypes as shuffling, trembling, or akinesias, with occurrence and frequency varying across patients. While deep learning (DL) models show promise in FOG detection, their robustness and generalization across subtypes are limited by data scarcity and imbalances between FOG/non-FOG classes and among subtypes. To address this, we propose a subtype-aware FOG augmentation technique enabling training of DL models to perform consistently across subtypes. Specifically, we introduce Hierarchical Coarse-to-Fine conditional Generative Adversary Network (Hi-CF cGAN), a two-stage model that generates subtype-conditioned FOG-like ankle accelerations that are realistic and diverse, as verified through visualization, UMAPs, and Maximum Mean Discrepancy comparison against real signals. We evaluate its effectiveness by training CNNs for FOG detection with both general (subtype-stratified) and personalized (subtype-variant, based on patient-specific subtype composition) augmentation via Hi-CF cGAN, benchmarking against classical augmentations and baseline (no augmentation). Compared to baseline, general augmentation with Hi-CF cGAN effectively improves average detection rates of FOG, trembling FOG, and especially the previously overlooked minor subtypes, shuffling FOG (from 66.8% to 81.6%) and akinesia FOG (from 58.7% to 77.9%). These improvements exceed those of classical augmentations, demonstrating superior realism, richness, and adaptability of Hi-CF cGAN-generated data in addressing FOG/non-FOG and subtype imbalances. Personalized augmentation further enhances accuracy on targeted subtype(s) compared to general augmentation, highlighting its potential for tailored model optimization.