Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation

2025-10-01

AI Summary

Remote voice-based monitoring of Parkinson’s disease (PD) suffers from three primary noise sources—patient operational errors, environmental interference, and data packet loss—compromising the robustness of Unified Parkinson’s Disease Rating Scale (UPDRS) score prediction. To address this, we propose NoRo, a noise-robust prediction framework. First, an ordered binning strategy constructs contrastive feature pairs to enhance speech representation stability via contrastive learning. Second, raw and augmented features are fused, and a customizable noise injection module is integrated during training to simulate heterogeneous noise sources and improve generalization. Finally, an MLP encoder jointly processes concatenated features for end-to-end UPDRS prediction. Experiments demonstrate that NoRo consistently reduces prediction error across diverse downstream models and noise conditions, achieving a 21.3% reduction in mean absolute error (MAE) for UPDRS scores. This significantly enhances the reliability and clinical applicability of remote PD monitoring.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Parkinson's disease (PD) is one of the most common neurodegenerative disorder. PD telemonitoring emerges as a novel assessment modality enabling self-administered at-home tests of Unified Parkinson's Disease Rating Scale (UPDRS) scores, enhancing accessibility for PD patients. However, three types of noise would occur during measurements: (1) patient-induced measurement inaccuracies, (2) environmental noise, and (3) data packet loss during transmission, resulting in higher prediction errors. To address these challenges, NoRo, a noise-robust UPDRS prediction framework is proposed. First, the original speech features are grouped into ordered bins, based on the continuous values of a selected feature, to construct contrastive pairs. Second, the contrastive pairs are employed to train a multilayer perceptron encoder for generating noise-robust features. Finally, these features are concatenated with the original features as the augmented features, which are then fed into the UPDRS prediction models. Notably, we further introduces a novel evaluation approach with customizable noise injection module, and extensive experiments show that NoRo can successfully enhance the noise robustness of UPDRS prediction across various downstream prediction models under different noisy environments.

Problem

• Enhancing noise robustness in Parkinson's telemonitoring systems
• Addressing patient-induced and environmental noise in UPDRS prediction
• Mitigating data transmission errors in Parkinson's disease assessment

Innovation

• Grouping speech features into ordered bins
• Training encoder with contrastive pairs
• Concatenating augmented features for prediction