Octave Mix: Data Augmentation Using Frequency Decomposition for Activity Recognition

Research in human activity recognition (HAR) requires a huge amount of data, but it is not easy to collect such measured sensor data. Besides, there is no much application of data augmentation (DA) in HAR. This study proposes Octave Mix as a novel synthetic-style DA method for sensor-based HAR. The proposed method uses frequency decomposition to intersect low- and high-frequency waveforms. In addition, we propose a DA ensemble method and a training algorithm to ensure robustness to the original sensor data while applicable to various feature representations. To evaluate the Octave Mix method's effectiveness, we conduct experiments using four different benchmark datasets of sensor-based HAR and achieve high estimation accuracy in our results. Furthermore, we demonstrate that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, achieves higher accuracy.