Octave Mix: Data augmentation using frequency decomposition for activity recognition
This research provides an incremental improvement in data augmentation techniques for sensor-based activity recognition, which is beneficial for researchers and practitioners dealing with scarce sensor data.
This paper addresses the challenge of limited sensor data in activity recognition by proposing Octave Mix, a data augmentation method that combines low and high frequency waveforms. The method, when combined with a DA ensemble model, achieved the best estimation accuracy across four benchmark datasets.
In the research field of activity recognition, although it is difficult to collect a large amount of measured sensor data, there has not been much discussion about data augmentation (DA). In this study, I propose Octave Mix as a new synthetic-style DA method for sensor-based activity recognition. Octave Mix is a simple DA method that combines two types of waveforms by intersecting low and high frequency waveforms using frequency decomposition. In addition, I propose a DA ensemble model and its training algorithm to acquire robustness to the original sensor data while remaining a wide variety of feature representation. I conducted experiments to evaluate the effectiveness of my proposed method using four different benchmark datasets of sensing-based activity recognition. As a result, my proposed method achieved the best estimation accuracy. Furthermore, I found that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, make it possible to achieve higher accuracy.