MGA-Net+: Acceleration-based packaging work recognition using motif-guided attention networks

Abstract

This study presents a new method for recognizing complex human activities within the logistics domain, such as packaging operations, using acceleration data from a body-worn sensor. The recognition of packaging tasks using standard supervised machine learning is complex because the observed data vary considerably depending on the number of items to be packed, the size of the items, and other parameters. In this study, we focused on the characteristics and necessary key actions (motions) that occur during a specific operation. For instance, when the packaging tape is stretched while assembling the shipping boxes. To focus on these characteristic actions when recognizing data, we propose the use of an attention-based neural network. With our method, the attention-based neural network’s training is guided such that its focus is on the motifs. In addition, this method was designed to accurately recognize short operations by leveraging data augmentation techniques. We tested our method on two logistics datasets and achieved a 3.9% improvement over the previous MGA-Net approach.