Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data

TL;DR

This paper introduces a novel time-series approach utilizing multimodal smartphone data to predict Parkinson's Disease, effectively handling noisy and irregular observations for improved remote health assessment.

Contribution

It presents a new method that synchronizes and enriches multimodal activity data with attention modules to enhance Parkinson's Disease prediction from noisy, irregular smartphone data.

Findings

Effective handling of noisy, irregular data

Improved prediction accuracy on public dataset

Robust temporal feature extraction

Abstract

Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.