Abstract
Human activity recognition (HAR) plays a crucial role in optimizing packing operations in industrial settings. However, HAR performance is often constrained by the limited availability of labeled data. To address this issue, we propose a novel data augmentation framework using conditional variational autoencoders (CVAE) to generate high-quality synthetic data. Our approach ensures class consistency while increasing data diversity by conditioning the generation process on activity labels. Additionally, we optimize hyperparameters for data generation using evolutionary computation, further improving recognition accuracy. The proposed method is validated on the OpenPack dataset, demonstrating its effectiveness in enhancing HAR performance without modifying the recognition model itself. Our key contributions include the introduction of a robust data augmentation pipeline, the application of CVAE for HAR, and the use of evolutionary computation to optimize data generation. Our model trained with data augmentation achieved an F1 score of 53.4%, while the recognition model trained without data augmentation achieved an F1 score of 48.1%.
