A Sense of Quality for Augmented Reality Assisted Process Guidance

Redzepagic A, Löffler C, Feigl T, Mutschler C (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: IEEE Xplore

Pages Range: 1-6

Conference Proceedings Title: IEEE Proc. International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

Event location: Ipojuca, Pernambuco BR

ISBN: 978-1-7281-7675-8

DOI: 10.1109/ISMAR-Adjunct51615.2020.00046

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 fine-grained and process-specific 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 modern 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 4.92mm and our classifier predicts the actions carried out with mean F1 value of 93.8% on average.

Authors with CRIS profile

Christoffer Löffler Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021 Lernende Methoden mit Konfidenzmaß für die Digitalisierung manueller Arbeitsprozesse auf geringer Datengrundlage (LZE KH) Feb. 1, 2019 - Dec. 31, 2020

Involved external institutions

Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Redzepagic, A., Löffler, C., Feigl, T., & Mutschler, C. (2020). A Sense of Quality for Augmented Reality Assisted Process Guidance. In IEEE Proc. International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 1-6). Ipojuca, Pernambuco, BR: IEEE Xplore.

MLA:

Redzepagic, Anes, et al. "A Sense of Quality for Augmented Reality Assisted Process Guidance." Proceedings of the International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Ipojuca, Pernambuco IEEE Xplore, 2020. 1-6.

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