# Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

## TL;DR

This paper explores data augmentation techniques for wearable sensor data to improve CNN-based classification of Parkinson's Disease motor states, addressing small datasets and variability.

## Contribution

It proposes new data augmentation methods tailored for wearable sensor data and demonstrates their effectiveness in enhancing CNN classification accuracy.

## Key findings

- Classification accuracy improved from 77.54% to 86.88%.
- Data augmentation significantly benefits small, noisy datasets.
- Enhanced robustness in Parkinson's Disease motor state classification.

## Abstract

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54\% to 86.88\%.