Arne Seeliger


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Seeliger

First Name

Arne

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0000-0001-7127-8104

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2021 - 20249
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Publications 1 - 9 of 9
  • Human Interventions in the Smart Factory – A Case Study on Co-Designing Mobile and Wearable Monitoring Systems with Manufacturing Staff
    Item type: Conference Paper
    Baldauf, Matthias; Müller, Sebastian; Seeliger, Arne; et al. (2021)
    CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
    So-called smart factories with networked physical machinery and highly automated manufacturing processes offer huge potential for efficiency and productivity increases. While respective user-centered research has been investigating assistance solutions for concrete maintenance or assembly tasks, this paper explores worker-oriented mobile and wearable systems for monitoring such complex and demanding manufacturing environments and for preparing for potential interventions. In four co-design workshops and focus groups, we investigated a manufacturing staff’s requirements for such monitoring systems and designed and evaluated low- and high-fidelity prototypes. Based on these insights, we derive a set of general design recommendations for mobile and wearable monitoring systems for smart factories.
  • Visualizing Time Series Data for Early Stage Business Analytics - The Case of Vehicle Telematics Data
    Item type: Conference Paper
    Bergmann, Svenja Marieke; Seeliger, Arne; Cenedese, Alberto (2022)
    Wirtschaftsinformatik 2022 Proceedings
    Applied business analytics (BA) projects commonly have time series data as a basis. While data analysts have the knowledge and tools to handle complex data, decision makers often struggle to develop an intuitive understanding of the data. In this paper, we highlight how data visualization carried out in the early stages of the BA process can help overcoming this issue. Our insights are based on a use case with a leading Swiss car retailer for which we derived driving behavior characteristics from vehicle telematics data. Specifically, we present three visualizations of raw and partially processed telematics data. We show how each visualization enriches interdisciplinary discussions and helps to create a common starting point for successive steps of the BA pipeline. Finally, we discuss the impact of the visualizations on the creation of new use cases and fast adoption.
  • Augmented reality for industrial quality inspection: An experiment assessing task performance and human factors
    Item type: Journal Article
    Seeliger, Arne; Cheng, Long; Netland, Torbjörn (2023)
    Computers in industry
    Augmented reality (AR) technologies promise to increase the flexibility and productivity of the workforce by providing real-time information to workers right where it is needed. Following Cognitive Load Theory and Attention Theory, AR assistance can reduce the burden on workers’ mental capacity while performing a task, thereby improving task performance. The benefits of AR on industrial activities like assembly or maintenance have been investigated extensively, but the literature on AR-assisted quality control, specifically, quality inspection, is scarce. This stands in contrast to the importance of industrial quality inspection and highlights the need for research on the topic. In this work, we develop an AR-based system for head-mounted displays (HMDs) that visualizes product defects directly on physical products. We investigate its effect on task performance and human factors through an experiment. Participants performed multiple quality inspections using real manufacturing products and equipment with the help of an AR HMD, a screen, or no additional help. We find increased task performance using the AR HMD system. In comparison to screen-based assistance, a moderating effect of task difficulty was observed. Specifically, AR was more beneficial to task completion times given a difficult task. Furthermore, the AR HMD system reduced mental workload and received a positive rating regarding user experience. Our work contributes to the research in industrial AR usage through a novel AR HMD system for quality inspection and provides so far missing empirical and theoretically grounded evidence on its benefits with regard to task performance and human factors.
  • Exploring the Effect of Visual Cues on Eye Gaze During AR-Guided Picking and Assembly Tasks
    Item type: Conference Paper
    Seeliger, Arne; Merz, Gerrit; Holz, Christian; et al. (2021)
    2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
    In this paper, we present an analysis of eye gaze patterns pertaining to visual cues in augmented reality (AR) for head-mounted displays (HMDs). We conducted an experimental study involving a picking and assembly task, which was guided by different visual cues. We compare these visual cues along multiple dimensions (in-view vs. out-of-view, static vs. dynamic, sequential vs. simultaneous) and analyze quantitative metrics such as gaze distribution, gaze duration, and gaze path distance. Our results indicate that visual cues in AR significantly affect eye gaze patterns. Specifically, we show that the effect varies depending on the type of visual cue. We discuss these empirical results with respect to visual attention theory.
  • Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave
    Item type: Journal Article
    Banholzer, Nicolas; van Weenen, Eva; Lison, Adrian; et al. (2021)
    PLoS ONE
    The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave.
  • Augmented Reality for Machine Setups: Task Performance and Usability Evaluation in a Field Test
    Item type: Conference Paper
    Seeliger, Arne; Netland, Torbjörn; Feuerriegel, Stefan (2022)
    Procedia CIRP ~ Leading manufacturing systems transformation - Proceedings of the 55th CIRP Conference on Manufacturing Systems 2022
    Head-mounted displays (HMDs) for augmented reality (AR) have received much scholarly attention. Most studies on AR HMDs, however, rely on lab-based environments or simplistic tasks that do not reflect the complexity of industrial work processes. In this work, we conducted a field study in which professional machine operators performed a setup task for an injection molding machine. We assessed and compared task performance, perceived workload, and usability of an AR HMD versus a tablet computer. We find that the use of an AR HMD is perceived as positive overall and not mentally demanding. However, task completion times were not reduced, although participants felt they were.
  • Context-Adaptive Visual Cues for Safe Navigation in Augmented Reality Using Machine Learning
    Item type: Journal Article
    Seeliger, Arne; Weibel, Raphael P.; Feuerriegel, Stefan (2024)
    International Journal of Human-Computer Interaction
    Augmented reality (AR) using head-mounted displays (HMDs) is a powerful tool for user navigation. Existing approaches usually display navigational cues that are constantly visible (always-on). This limits real-world application, as visual cues can mask safety-critical objects. To address this challenge, we develop a context-adaptive system for safe navigation in AR using machine learning. Specifically, our system utilizes a neural network, trained to predict when to display visual cues during AR-based navigation. For this, we conducted two user studies. In User Study 1, we recorded training data from an AR HMD. In User Study 2, we compared our context-adaptive system to an always-on system. We find that our context-adaptive system enables task completion speeds on a par with the always-on system, promotes user autonomy, and facilitates safety through reduced visual noise. Overall, participants expressed their preference for our context-adaptive system in an industrial workplace setting.
  • Ubiquitous Machinery Monitoring – A Field Study on Manufacturing Workers’ User Experience of Mobile and Wearable Monitoring Apps
    Item type: Journal Article
    Müller, Sebastian; Baldauf, Matthias; Seeliger, Arne (2022)
    Proceedings of the ACM on Human-Computer Interaction
    Fueled by ongoing digitization efforts, manufacturing is currently undergoing a transformational process towards interconnected machinery and workforce, which enables a wide range of interactive monitoring and controlling applications. Whereas existing user-centered work addressed remote monitoring from office workplaces, it remains unclear how manufacturing workers experience and adopt machinery monitoring apps on mobile and wearable devices. To close this gap, we conducted a four-week field study in a running factory to study workers’ overall user experience and acceptance of such monitoring apps, the subjective impact on their work routines, and their preferred device type. Under productive operation, 11 manufacturing workers used functional application prototypes on smartphones and smartwatches to receive notifications of machine incidents. In 22 individual interviews and two focus groups, we collected the participants’ impressions and assessments. Based on these results, we derive a set of recommendations for designing and deploying machinery monitoring apps for manufacturing workers.
Publications 1 - 9 of 9