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This research investigates bias in wearable-based human activity recognition models used to detect freezing of gait in Parkinson's disease, revealing significant performance disparities across different patient phenotypes and demographics. The study demonstrates that transfer learning approaches effectively mitigate these fairness issues better than conventional methods like adversarial debiasing or threshold optimization, offering a pathway toward more equitable clinical monitoring tools.
Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation
arXiv:2502.09626v3 Announce Type: replace-cross Abstract: Freezing of gait (FOG) is a debilitating feature of Parkinson's disease (PD) and a common cause of injurious falls. Recent advances in wearable-based human activity recognition (HAR) have enabled FOG detection, but bias and fairness in these models remain understudied. Bias is defined as a systematic error 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, widening care disparities. We evaluated the bias and fairness of state-of-the-art HAR models for FOG detection across FOG phenotypes and patient demographics using 4 public wearable datasets (145 subjects and 59.8 hours of recording) with multiple on-body sensor locations and temporal annotations on FOG episodes across various behavioral test protocols. Four mitigation approaches were assessed: conventional methods (threshold optimization and adversarial debiasing) and two transfer learning-based approaches (multi-site transfer and fine-tuning pre-trained models). Fairness was primarily quantified using the demographic parity ratio (DPR) and the equalized odds ratio (EOR). HAR models exhibited substantial bias (DPR & EOR < 0.8) across age, sex, disease duration, and critically, FOG phenotype. This phenotype-specific bias is particularly concerning as tremulous and akinetic FOG may require different clinical management. Conventional methods failed: threshold optimization (DPR=-0.126, EOR=+0.063) and adversarial debiasing (DPR=-0.008, EOR=-0.001). In contrast, multi-site transfer learning improved fairness (DPR=+0.037, p<0.01; EOR=+0.045, p<0.01) and performance (F1-score=+0.020, p<0.05), though neither metric reached 0.8. Transfer learning across diverse datasets may partially mitigate these disparities, helping wearable-based FOG monitoring benefit all individuals with PD.
Abstract:Freezing of gait (FOG) is a debilitating feature of Parkinson's disease (PD) and a common cause of injurious falls. Recent advances in wearable-based human activity recognition (HAR) have enabled FOG detection, but bias and fairness in these models remain understudied. Bias is defined as a systematic error 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, widening care disparities. We evaluated the bias and fairness of state-of-the-art HAR models for FOG detection across FOG phenotypes and patient demographics using 4 public wearable datasets (145 subjects and 59.8 hours of recording) with multiple on-body sensor locations and temporal annotations on FOG episodes across various behavioral test protocols. Four mitigation approaches were assessed: conventional methods (threshold optimization and adversarial debiasing) and two transfer learning-based approaches (multi-site transfer and fine-tuning pre-trained models). Fairness was primarily quantified using the demographic parity ratio (DPR) and the equalized odds ratio (EOR). HAR models exhibited substantial bias (DPR & EOR < 0.8) across age, sex, disease duration, and critically, FOG phenotype. This phenotype-specific bias is particularly concerning as tremulous and akinetic FOG may require different clinical management. Conventional methods failed: threshold optimization (DPR=-0.126, EOR=+0.063) and adversarial debiasing (DPR=-0.008, EOR=-0.001). In contrast, multi-site transfer learning improved fairness (DPR=+0.037, p<0.01; EOR=+0.045, p<0.01) and performance (F1-score=+0.020, p<0.05), though neither metric reached 0.8. Transfer learning across diverse datasets may partially mitigate these disparities, helping wearable-based FOG monitoring benefit all individuals with PD.
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2502.09626 [eess.SP]
(or arXiv:2502.09626v3 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2502.09626 Focus to learn more arXiv-issued DOI via DataCite
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