Acceleration-based Human Activity Recognition of Packaging Tasks Using Motif-guided Attention Networks

This study presents a new method for recognizing complex human activities in a logistical domain, such as packaging, using acceleration data from a body-worn sensor. Recognition of packaging tasks using standard supervised machine learning is difficult because the observed data vary considerably depending on the number of items to pack, the size of the items, and other parameters. In this study, we focus on characteristic and necessary actions (motions) that occur in a specific operation such as an action of stretching packing tape when assembling shipping boxes. We propose the use of an attention-based neural network to focus on these characteristic actions when recognizing the data. However, training of a such deep network model is a data-intensive process, and obtaining a huge amount of labeled training data in actual industrial settings is difficult. To address this problem, we employ motif-detection algorithms to detect sensor data motifs (segments corresponding to characteristic actions) that can be useful for recognizing operations in advance. Moreover, we propose that the training of the attention-based network should be guided such that it pays attention to the detected motifs, i.e., motif-guided training.