ModifyAug: Data Augmentation for Virtual IMU Signal based on 3D Motion Modification Used for Real Activity Recognition
Lingtao Huang, Chengshuo Xia · 2024
In wearable human activity recognition (HAR), the generation and utilization of virtual IMU data has recently gained attention. The use of virtual data can improve the robustness, effective features, and customized motion types chosen in the HAR system. However, few studies have focused on augmenting virtual IMU data to reduce the dependence on real IMU data during the machine learning model training phase. This work proposes modifying the reconstructed 3D motion by its joint data to generate larger 3D motion based on a given 3D motion sequence, thus augmenting the virtual IMU dataset. The method simulates the intra-difference of the same motion type in the real world. It aims to use fewer 3D motion inputs to generate the larger size of the virtual IMU dataset to recognize the real activity. The experiment demonstrated the feasibility of the proposed method and provided insight into the 3D motion modification-based augmentation method.