  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.
