{"id":112904,"date":"2026-02-05T09:33:56","date_gmt":"2026-02-05T08:33:56","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=112904"},"modified":"2026-02-10T22:00:57","modified_gmt":"2026-02-10T21:00:57","slug":"ai-assembly-workplace-design","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/ai-assembly-workplace-design\/","title":{"rendered":"Applied AI for Human-Centric Assembly Workplace Design"},"content":{"rendered":"\n

Assembly workstations are advancing quickly as artificial intelligence (AI) becomes more integrated into industrial production settings. Specifically, the ability to predict human movements and identify features within a workspace is key for optimizing assembly<\/a> tasks, tracking progress, and enabling flexible setups. With the increased use of collaborative robots, the traditional fixed automation model is shifting toward human-centered automation, where people and machines collaborate closely on shared tasks. This approach is particularly beneficial in high-mix, low-volume production, where flexibility and adaptability are essential.<\/p>\n\n\n\n

Effective human-robot collaboration (HRC) depends on the robot\u2019s ability to anticipate human actions. Achieving this requires systems that can sense and interpret body movements, gestures, gaze, and posture in real time. Manual interpretation of such behaviors is often slow and context-dependent, making AI-powered solutions essential. Predictive models that process multimodal sensor data, such as skeletal joint trajectories and eye-tracking signals, allow systems to infer both the physical actions and the intent behind them. These capabilities transform conventional workstations into intelligent, responsive environments.<\/p>\n\n\n\n

Smart workstations, which combine human input and robotic support, are designed to adapt to user behavior by providing proactive assistance. While AI-driven support in controlled environments is well established, challenges remain in applying these systems to real-world production. Some of these challenges include model interpretation, transparency of AI decisions, and compliance with emerging regulations. Specifically, predictive AI systems that model human behavior need to be evaluated to ensure they align with ethical standards, such as the European Union\u2019s Artificial Intelligence Act, which categorizes such systems in HRC as \u201chigh-risk.\u201d This classification requires adherence to strict guidelines on human oversight, data protection, fairness, and transparency [1].<\/p>\n\n\n\n

In this work, we introduce a holistic and systematic approach for applying AI ethics assessment to an industrial use case based on a Smart Work Assistant (SWA) system that supports human workers in assembly. The approach combines multimodal sensing and AI to predict human intention and focus through motion and gaze data. In addition to the technical implementation, we assess the system\u2019s ethical and regulatory implications using the Z-Inspection\u00ae methodology [2]. Originally designed to evaluate trustworthy AI, Z-Inspection\u00ae has been demonstrated in sectors such as healthcare and offers a structured way to assess alignment with legal, social, and technical standards.<\/p>\n\n\n\n

This paper introduces initial ideas for applying Z-Inspection\u00ae in an industrial assembly environment. We explore whether this methodology can effectively support ethical assessments of predictive AI in dynamic, physically interactive settings, where safety, accountability, and human involvement are essential. While large-scale testing is still in progress, preliminary observations indicate notable improvements, such as clearer task guidance, less physical strain, and maintained operator control. These insights are distilled into practical and adaptable principles for AI-enhanced assembly work. Results are based on initial qualitative observations from pilot use; plans for quantitative validation, including posture-angle, task duration, and operator comfort ratings, are underway.<\/p>\n\n\n\n

Current developments in AI for human-centric assembly<\/h2>\n\n\n\n

AI for smart work assistants in assembly offers significant benefits in terms of operational efficiency, human-robot collaboration, and worker well-being. For instance, it can optimize manufacturing processes by adapting assembly lines to fluctuating product designs and market demands, enhance equipment life cycle management through predictive maintenance, and optimize workforce allocation based on skills and production needs [3]. The role of AI in defect inspection and product quality is crucial for improving the product life cycle, from design to after-sales support [4].<\/p>\n\n\n\n

In a human-robot collaboration (HRC) scenario, [5] proposed human action recognition (HAR) in assembly lines to enhance HRC efficiency. This framework has been validated in real-world applications, demonstrating its effectiveness in enhancing human-robot interactions. Similarly, conversation models proposed in [6<\/a>] create a more natural human-robot interaction in real-world scenarios. In [7<\/a>], digital intelligent assistants (DIAs) are proposed, which leverage large language models (LLMs) to reduce cognitive workload while improving user experience in assembly processes.<\/p>\n\n\n\n

AI can identify potential ergonomic issues in manufacturing processes, preventing workplace injuries and long-term medical problems. Automated solutions analyze operators\u2019 work allocations to ensure ergonomic safety, adapting to changes in line speed or product mix [8]. The integration of AI in manufacturing shifts the role of humans from merely interacting with technology to actively collaborating with AI-enabled agents. This shift necessitates a comprehensive understanding of human-AI collaboration to design systems that support effective production and operator well-being [9].<\/p>\n\n\n\n

Integrating AI solutions into manufacturing processes poses challenges related to data integration and scalability. Addressing these challenges is crucial for the widespread adoption of AI in manufacturing [3]. Designing AI-enabled systems that consider human input at the design stage can improve the effectiveness of these systems in production environments [10]. In this regard, challenges related to data integration, scalability, and ethical considerations must be addressed to fully realize the potential of AI in this domain. Future research should focus on developing predictive models, optimizing equipment replacement strategies, and designing human-AI collaborative systems that genuinely support effective production and operator well-being.<\/p>\n\n\n\n

Human-centered applications such as human-robot collaboration (HRC) employ advanced sensor technology and machine learning to predict human motion behaviors using time-series data. They thereby improve robot responsiveness and prevent collisions. There are several human motion-modeling techniques including deep learning [11], Gaussian mixture models [12], and motion diffusion models [13]. Gaussian Mixture Models (GMMs) and their derivatives have been shown to handle temporal variability and multimodal distributions well [12, 14]. Similarly, attention-based networks are promising for modeling sequential data in tasks involving gesture and motion prediction [15].<\/p>\n\n\n\n

Ethically, several frameworks have been proposed to ensure responsible AI deployment. The EU\u2019s High-Level Expert Group on AI introduced guidelines on Trustworthy AI, emphasizing human agency, transparency, privacy, and non-discrimination. However, these guidelines lack concrete implementation strategies, particularly in dynamic industrial contexts. The Z-Inspection\u00ae<\/sup> methodology fills this gap by providing a structured, iterative process for assessing AI systems through legal, ethical, and technical lenses. Applied initially in healthcare AI, recent work points out its potential adaptability to industrial and manufacturing contexts (see [2]).<\/p>\n\n\n\n

Despite these advances, a gap remains in integrating predictive AI models with holistic ethical assessments in real-world industrial settings. Our work addresses this by combining technical innovation with regulatory alignment, using a scenario of the ELAM system, a digital worker assistance system developed by Armbruster Engineering.<\/p>\n\n\n\n

Proposed method for AI-supported motion capture system<\/h2>\n\n\n\n

In this work, we propose a method for applied AI based on a modular pipeline that employs human motion modeling, inference, and predictive human motion understanding for assistive smart workstations using the Z-Inspection\u00ae trustworthy AI assessment. The Z-Inspection\u00ae process involves three main phases: setup, access, and resolution. The setup phase includes preconditions, team, boundaries, and contexts, whereas the access phase expands to socio-technical scenarios, ethical issues, a map of ethical tensions according to regulations, and the definition and execution of action paths. In the resolution phase, solutions are resolved and recommended.<\/p>\n\n\n\n

Our approach applies the Z-Inspection\u00ae method to AI-driven digital human motion analysis, thereby improving process monitoring by identifying manual assembly tasks that typically lack sensor-based documentation. This allows ELAM to effectively record these actions and adapt flexibly to different tasks without hardware changes. Moreover, ergonomic monitoring through digital human motion analysis can automatically adjust workstations to reduce physical strain. Analyzing digital motion data can also lead to better workstation layouts by strategically positioning frequently accessed components, thus improving efficiency. Additionally, capturing complete assembly sequences digitally could streamline the creation of accurate work instructions, making initial setups and future adjustments easier. In our proposed pipeline (see Fig. 1<\/strong>), the three phases are contained within the Z-Inspection\u00ae access phase.<\/p>\n\n\n\n

Phase I – Inference and predictive AI<\/h3>\n\n\n\n

Phase I involves constructing an offline primitive motion model using Morphable Graphs (see [12]). A motion capture system captures the skeletal trajectories of a worker during typical assembly tasks. The primitive motion models are created based on the extended work, initially presented in a new planning approach by Manns et al. [16], which combined a Gaussian Mixture Model (GMM) and Functional Principal Component Analysis (FPCA) to create probabilistic primitive motion models (see also [13]). \u00a0Each primitive is encoded as a structured entry in a knowledge representation module, forming a domain-specific motion library.<\/p>\n\n\n\n

In this phase, real-time motion capture data is projected into the learned FPCA space. A likelihood-based inference mechanism classifies the observed motion segment and predicts future frames. The system not only identifies which primitive is being executed, but also forecasts the likely subsequent action, enabling anticipatory behavior in robotic assistants. The red dashed lines in Figure 1<\/strong> highlight the real-time test and inference paths of this phase.<\/p>\n\n\n\n

\"Figure
Figure 1: Overview of the proposed predictive motion AI system integrated with the ELAM platform. The system combines Morphable Graph modeling for motion primitive learning with a real-time inference engine, governed by an applied ethics layer based on Z-Inspection\u00ae.<\/em><\/figcaption><\/figure>\n\n\n\n

Phase II – \u201cApplied AI Ethics\u201d assessment through morphological matrix<\/h3>\n\n\n\n

To ensure that predictive AI complies with the EU AI Act and promotes trustworthy and explainable AI, especially in smart workplaces, a holistic assessment for applied AI ethics and trustworthiness is proposed based on the Z-Inspection\u00ae methodology. This assessment is part of motion capture support for AI in ELAM, which continuously monitors the system\u2019s ethical, legal, and technical aspects.<\/p>\n\n\n\n

The key ethical considerations to address during this assessment include: <\/p>\n\n\n\n