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.
翻译:在活动识别的研究领域,虽然难以收集大量测量的传感器数据,但对数据增强(DA)没有进行多少讨论。在本研究中,我提议Octave Mix作为基于传感器的活动识别的一种新型合成式DA方法。Octave Mix是一种简单的DA方法,它通过利用频率分解将低频和高频波形相互交叉,将两种波形结合起来。此外,我提议DA组合模型及其培训算法,以便在保持多种特征代表的同时,获得原始传感器数据的稳健性。我进行了实验,利用基于遥感活动识别的四个不同的基准数据集评估我提议的方法的有效性。结果,我提议的方法实现了最佳的估计准确性。此外,我发现,将两种DA战略组合为:Octave Mix与旋转混合,从而有可能实现更高的准确性。