# Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning

## TL;DR

This paper explores deep learning models for monitoring Parkinson's disease severity using wearable sensor data, demonstrating their superiority over classical methods and proposing techniques to handle data limitations and class imbalance.

## Contribution

It introduces a comprehensive deep learning approach for Parkinson's severity detection, including transfer learning, custom evaluation metrics, and validation schemes tailored for medical data.

## Key findings

- Deep learning models outperform classical machine learning on sensor data.
- Treating the problem as regression yields better results than classification.
- Transfer learning significantly improves model performance.

## Abstract

One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device. We find that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task. Furthermore, our results suggest that treating this problem as a regression instead of an ordinal regression or a classification task is most appropriate. For consistent model evaluation and training, we adopt the leave-one-subject-out validation scheme to the training of deep learning models. We also employ a class-weighting scheme to successfully mitigate the problem of high multi-class imbalances in this domain. In addition, we propose a customized performance measure that reflects the requirements of the involved medical staff on the model. To solve the problem of limited availability of high quality training data, we propose a transfer learning technique which helps to improve model performance substantially. Our results suggest that deep learning techniques offer a high potential to autonomously detect motor states of patients with Parkinson's disease.