Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation

TL;DR

This study evaluates bias in wearable-based freezing of gait detection models for Parkinson's disease, finding transfer learning approaches improve fairness and performance across diverse patient groups and phenotypes.

Contribution

It systematically assesses bias in FOG detection models and demonstrates transfer learning as an effective method for bias mitigation and fairness enhancement.

Findings

HAR models show bias across age, sex, and FOG phenotypes.

Conventional bias mitigation methods are ineffective.

Transfer learning improves fairness and detection accuracy.

Abstract

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) and a common cause of injurious falls. Recent advances in wearable-based human activity recognition (HAR) enable FOG detection, but bias and fairness in these models remain understudied. Bias refers to systematic errors leading to unequal outcomes, while fairness refers to consistent performance across subject groups. Biased models could systematically underserve patients with specific FOG phenotypes or demographics, potentially widening care disparities. We systematically evaluated bias and fairness of state-of-the-art HAR models for FOG detection across phenotypes and demographics using multi-site datasets. We assessed four mitigation approaches: conventional methods (threshold optimization and adversarial debiasing) and transfer learning approaches (multi-site transfer and fine-tuning large pretrained models). Fairness was quantified using demographic parity ratio (DPR) and equalized odds ratio (EOR). HAR models exhibited substantial bias (DPR & EOR < 0.8) across age, sex, disease duration, and critically, FOG phenotype. Phenotype-specific bias is particularly concerning as tremulous and akinetic FOG require different clinical management. Conventional bias mitigation methods failed: threshold optimization (DPR=-0.126, EOR=+0.063) and adversarial debiasing (DPR=-0.008, EOR=-0.001) showed minimal improvement. In contrast, transfer learning from multi-site datasets significantly improved fairness (DPR=+0.037, p<0.01; EOR=+0.045, p<0.01) and performance (F1-score=+0.020, p<0.05). Transfer learning across diverse datasets is essential for developing equitable HAR models that reliably detect FOG across all patient phenotypes, ensuring wearable-based monitoring benefits all individuals with PD.