Octave Mix: Data augmentation using frequency decomposition for activity recognition

TL;DR

This paper introduces Octave Mix, a novel data augmentation technique for sensor-based activity recognition that uses frequency decomposition to improve model robustness and accuracy across multiple datasets.

Contribution

The paper proposes Octave Mix, a new synthetic data augmentation method based on frequency decomposition, and an ensemble training approach to enhance activity recognition performance.

Findings

Octave Mix outperforms existing augmentation methods in accuracy.

Ensembling Octave Mix with mixup and rotation further improves results.

The method is validated on four benchmark datasets.

Abstract

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.