  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\%.
