Abstract

The ongoing automation of modern production processes requires novel human-computer interaction concepts that support employees in dealing with the unstoppable increase in time pressure, cognitive load, and the required line-grained and process-speci tic knowledge. Augmented Reality (AR) systems support employees by guiding and teaching work processes. Such systems still lack a precise process quality analysis (monitoring), which is, however, crucial to close gaps in the quality assurance of industrial processes. We combine inertial sensors, mounted on work tools, with AR headsets to enrich modem assistance systems with a sense of process quality. For this purpose, we develop a Machine Learning (ML) classifier that predicts quality metrics from a 9-degrees of freedom inertial measurement unit, while we simultaneously guide and track the work processes with a HoloLens AR system. In our user study, 6 test subjects perform typical assembly tasks with our system. We evaluate the tracking accuracy of the system based on a precise optical reference system and evaluate the classification of each work step quality based on the collected ground truth data. Our evaluation shows a tracking accuracy of fast dynamic movements of 4.92 mm and our classifier predicts the actions carried out with mean F1 value of 93.8% on average.