Enhancing Noise Robustness of Parkinson's Disease Telemonitoring via Contrastive Feature Augmentation
This work addresses noise robustness in telemonitoring for Parkinson's disease patients, offering an incremental improvement to existing prediction methods.
The paper tackles the problem of noise in Parkinson's disease telemonitoring, which causes higher prediction errors for UPDRS scores, and proposes NoRo, a framework that uses contrastive feature augmentation to enhance noise robustness, showing successful improvements across various models and noisy environments.
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.