Autor: Henning Vogler

Applied AI for Human-Centric Assembly Workplace Design

Applied AI for Human-Centric Assembly Workplace Design

An ethics-informed approach
Tadele Belay Tuli ORCID Icon, Michael Jonek ORCID Icon, Sascha Niethammer, Henning Vogler, Martin Manns ORCID Icon
Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection®. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844/I4SE.26.1.58
Human-Centered Assistance Systems

Human-Centered Assistance Systems

Systematic evaluation of assembly assistance systems
Dennis Keiser, Christoph Petzoldt, Thies Beinke, Michael Freitag ORCID Icon, Henning Vogler
The employee remains a key productivity element in industrial assembly. Assembly assistance systems have therefore become an integral part of employee support. This paper presents a novel assistance system that complements process-related assistance with human-centered functionalities. In addition, an approach for the systematic evaluation of assembly assistance systems is presented in this paper. The research is based on an evaluation of the current state of the art through systematic market analysis of available assembly assistance systems.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 3 | Pages 11-15