Octave Mix: Data augmentation using frequency decomposition for activity recognition

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.

arXiv comment12 pages, 4 figures, this paper is a pre-print to submit to IEEE Access journal

References in corpus (8)

• Deep Learning for Sensor-based Activity Recognition: A Survey
• Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors
• Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks
• Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
• Style Augmentation: Data Augmentation via Style Randomization
• Asymmetric Residual Neural Network for Accurate Human Activity Recognition
• DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition
• Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data