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

arXiv: https://arxiv.org/abs/2502.09626 · HTML: https://mlsanity.com/paper/2502.09626/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [Fairlearn: Assessing and Improving Fairness of AI Systems](https://mlsanity.com/paper/2303.16626/) (2023)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks](https://mlsanity.com/paper/2103.15449/) (2021)
- [A Foundation Model for Wearable Movement Data in Mental Health Research](https://mlsanity.com/paper/2411.15240/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)
- [Fairness in Machine Learning-based Hand Load Estimation: A Case Study on Load Carriage Tasks](https://mlsanity.com/paper/2504.05610/) (2025)

## Abstract (arXiv)

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.