부산대학교 도서관

Over the past decade, deep learning developed rapidly and had significant impact on a variety of application domains. It has been applied to the field of human activity recognition to substitute for well-established analysis techniques that rely on handcrafted feature extraction and classification methods in recent years. However, less attentions have been paid to the influence of training data on recognition accuracy. In this paper, we assessed the influence factors of data volume and data balance in human activity recognition when using deep learning approaches. We evaluated the relationship between data volumes of training dataset and predict accuracy of deep learning algorithms. Given the impact of the data balance between activity categories on the recognition accuracy, we modified the SMOTE algorithm so that it can be applied to human activity recognition. Results show that when the data volume is small (