Abstract
Machine learning algorithms depend heavily on the availability of data. Since data collection can be both time-consuming and costly, virtual data generation and augmentation techniques are commonly used. Given that inertial measurement unit (IMU) datasets for industrial human activity recognition (HAR) are typically small, we propose an approach that combines traditional augmentation techniques with class-independent and class-dependent sliding window generation to enhance the performance of the given HAR classifier. This work was part of the Virtual Data Generation for Complex Industrial Activity Recognition challenge. We consider five augmentation methods and three resampling methods. Our experiments demonstrate an improvement of 7.04 percentage points over the baseline accuracy of 55.89%. The code is available on GitHub https://github.com/b02a8S0E/ABCDChallengePacking.
https://ror.org/01ayc5b57 
