Data Augmentation for Human Activity Recognition

• Main Authors: Tou, Chih-Hao, 竇志浩
• Other Authors: Chen, Wen-Hui
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/uef6dx

碩士 === 國立臺北科技大學 === 自動化科技研究所 === 106 === Due to the advances in technology and hardware development, deep learning has become a popular research topic in recent years. In comparison with traditional machine learning algorithms, deep learning based approaches have better performance in many tasks. However, the application of deep learning requires ample data and a well-designed neural network architecture. When the training data is not enough or limited, we do not normally obtain good results using deep learning approaches. This thesis proposed an approach based on the fusion of deep learning and data augmentation to improve the activity recognition accuracy. We proposed five methods for data augmentation, including noise injection, permutation, rotation, and two different generative models to reach a high-performance model with limited training data. The experimental results showed that the proposed data augmentation approach could reach an accuracy of 98.32% on a UCI dataset, and boost the accuracy up to 1.5% when compared with data without augmentation.