The Effects of Data Augmentation Methods on the Performance of Human Activity Recognition

Abstract

In recent developments of Human Activity Recognition systems (HAR), It has been found that deep learning models are being studied by researchers, especially convolutional neural networks integrated with long shortterm memory cells such as convolutional LSTM (ConvLSTM) networks. The deep structures require large datasets which demand extensive data collection. Therefore, various data augmentation methods are under focus nowadays. Furthermore, the challenge of time-series data augmentation is to choose the method that preserves the correct labels. In this paper, we evaluated and compared six data augmentation methods (i.e., autoencoder, time warping, amplitude warping, scaling, jittering, and linear combination) utilizing ConvLSTM networks for classification. Consequently, the WISDM dataset (tri-axial accelerometer signals of six activities) was augmented to the final size of 1.5 times the original dataset. Further, the proposed ConvLSTM model was trained seven times (once with the raw dataset and six times with the augmented dataset). The results indicated classification accuracy improvements for the test data from 92% to 93%, 97% and 98%, when training the models using augmented datasets, augmented using linear combination, scaling, and jittering methods respectively. Activity-wise analysis suggested the stairing activities to be the most challenging for the model to classify when the dataset was augmented by time warping, amplitude warping as well as autoencoder.