Deep learning has shown excellent performance in behavior classification. However, in return, they typically require a considerably larger amount of data to perform well. The cost of data collection is significantly high. This work proposed a random rotation-based data augmentation method with a low cost of data collection and evaluated it using neural networks. More data augmentation techniques in a way that fits the characteristics of animal behavioral data are being investigated for further improvement considered for the real application.