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

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