Virtual Data Generation for Complex Industrial Activity Recognition : An Approach Based on Interpolation Algorithms and Data Augmentation Techniques

Abstract

In this study, we propose a virtual data generation method tailored for the OpenPack Challenge, which leverages acceleration sensor data along with associated operation and action labels. Our approach integrates simple yet effective data augmentation techniques (e.g., jitter, axis switching, and time warping) with advanced interpolation algorithms (including various forms of RBF and FFT-based methods) to generate high-quality virtual data. Experimental findings indicate that data-driven methods specifically adapted to the characteristics of sensor signals can substantially improve HAR model performance, offering a promising solution in scenarios where large-scale data collection is impractical. Specifically, by grouping the data according to the action and operation labels and applying an RBF-based interpolation algorithm to each group, an F1 score of 0.6379 was achieved. However, employing more advanced data generation algorithms such as GANs may further improve performance.