Now showing 1 - 10 of 12
  • Publication
    EToS-1: Eye Tracking on Shopfloors for User Engagement with Automation
    (CEUR Workshop Proceedings, 2022-04-30) ; ; ;
    Stolze, Markus
    Mixed Reality (MR) is becoming an integral part of many context-aware industrial applications. In maintenance and remote support operations, the individual steps of computer-supported (cooperative) work can be defined and presented to human operators through MR headsets. Tracking of eye movements can provide valuable insights into a user’s decision-making and interaction processes. Thus, our overarching goal is to better understand the visual inspection behavior of machine operators on shopfloors and to find ways to provide them with attention-aware and context-aware assistance through MR headsets that increasingly come with eye tracking (ET) as a default feature. Toward this goal, in two industrial scenarios, we used two mobile eye tracking devices and systematically compared the visual inspection behavior of novice and expert operators. In this paper we present our preliminary findings and lessons learned.
  • Publication
    MR Object Identification and Interaction: Fusing Object Situation Information from Heterogeneous Sources
    (ACM, 2023-09-28) ;
    Khakim Akhunov
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    Federico Carbone
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    Kasim Sinan Yildirim
    The increasing number of objects in ubiquitous computing environments creates a need for effective object detection and identification mechanisms that permit users to intuitively initiate interactions with these objects. While multiple approaches to such object detection-including through visual object detection, fiducial markers, relative localization, or absolute spatial referencing-are available, each of these suffers from drawbacks that limit their applicability. In this paper, we propose ODIF, an architecture that permits the fusion of object situation information from such heterogeneous sources and that remains vertically and horizontally modular to allow extending and upgrading systems that are constructed accordingly. We furthermore present BLEARVIS, a prototype system that builds on the proposed architecture and integrates computer-vision (CV) based object detection with radio-frequency (RF) angle of arrival (AoA) estimation to identify BLE-tagged objects. In our system, the front camera of a Mixed Reality (MR) head-mounted display (HMD) provides a live image stream to a vision-based object detection module, while an antenna array that is mounted on the HMD collects AoA information from ambient devices. In this way, BLEARVIS is able to differentiate between visually identical objects in the same environment and can provide an MR overlay of information (data and controls) that relates to them. We include experimental evaluations of both, the CV-based object detection and the RF-based AoA estimation, and discuss the applicability of the combined RF and CV pipelines in different ubiquitous computing scenarios. This research can form a starting point to spawn the integration of diverse object detection, identification, and interaction approaches that function across the electromagnetic spectrum, and beyond.
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    Scopus© Citations 2
  • Publication
    Pupillometry for Measuring User Response to Movement of an Industrial Robot
    ( 2023-05-30)
    Damian Hostettler
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    Interactive systems can adapt to individual users to increase productivity, safety, or acceptance. Previous research focused on different factors, such as cognitive workload (CWL), to better understand and improve the human-computer or human-robot interaction (HRI). We present results of an HRI experiment that uses pupillometry to measure users' responses to robot movements. Our results demonstrate a significant change in pupil dilation, indicating higher CWL, as a result of increased movement speed of an articulated robot arm. This might permit improved interaction ergonomics by adapting the behavior of robots or other devices to individual users at run time. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing systems and tools.
  • Publication
    ShoppingCoach: Using Diminished Reality to Prevent Unhealthy Food Choices in an Offline Supermarket Scenario
    Non-communicable diseases, such as obesity and diabetes, have a significant global impact on health outcomes. While governments worldwide focus on promoting healthy eating, individuals still struggle to follow dietary recommendations. Augmented Reality (AR) might be a useful tool to emphasize specific food products at the point of purchase. However, AR may also add visual clutter to an already complex supermarket environment. Instead, reducing the visual prevalence of unhealthy food products through Diminished Reality (DR) could be a viable alternative: We present Shopping-Coach, a DR prototype that identifies supermarket food products and visually diminishes them dependent on the deviation of the target product’s composition from dietary recommendations. In a study with 12 participants, we found that ShoppingCoach increased compliance with dietary recommendations from 75% to 100% and reduced decision time by 41%. These results demonstrate the promising potential of DR in promoting healthier food choices and thus enhancing public health.
  • Publication
    SOCRAR: Semantic OCR through Augmented Reality
    To enable people to interact more efficiently with virtual and physical services in their surroundings, it would be beneficial if information could more fluently be passed across digital and non-digital spaces. To this end, we propose to combine semantic technologies with Optical Character Recognition on an Augmented Reality (AR) interface to enable the semantic integration of (written) information located in our everyday environments with Internet of Things devices. We hence present SOCRAR, a system that is able to detect written information from a user’s physical environment while contextualizing this data through a semantic backend. The SOCRAR system enables in-band semantic translation on an AR interface, permits semantic filtering and selection of appropriate device interfaces, and provides cognitive offloading by enabling users to store information for later use. We demonstrate the feasibility of SOCRAR through the implementation of three concrete scenarios.
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  • Publication
    NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR System
    (ACM, 2024-05-28)
    Aleksandar Slavuljica
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    Humans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.
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  • Publication
    Telelife: The Future of Remote Living
    (Frontiers, 2021-11-29)
    Orlosky, Jason
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    Sra, Misha
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    Peng, Huaishu
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    Kim, Jeeeun
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    Kos'myna, Nataliya
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    Höllerer, Tobias
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    Steed, Anthony
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    Kiyokawa, Kiyoshi
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    Akşit, Kaan
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    Steinicke, Frank
    In recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected almost all aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has exacerbated the fact that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce “Telelife,” a vision for the near and far future that depicts the potential means to improve remote living and better align it with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual/physical rapid prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics and haptic feedback, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, we provide a meta-synthesis in this paper that will guide future research on remote living.
  • Publication
    Telelife: A Vision of Remote Living in 2035
    (ACM, 2022-05-05) ;
    Kim, Jeeeun
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    Peng, Huaishu
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    Kiyokawa, Kiyoshi
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    Steed, Anthony
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    Hollerer, Tobias
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    Kos’myna, Nataliya
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    Sra, Misha
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    Orlosky, Jason
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    Akşit, Kaan
  • Publication
    Gaze-enabled activity recognition for augmented reality feedback
    ( 2024-03-16) ; ; ; ;
    Andrew Duchowski
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    Krzysztof Krejtz
    Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material.
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  • Publication
    GEAR: Gaze-enabled augmented reality for human activity recognition
    (ACM, 2023-05-30) ; ; ; ;
    Hermann, Jonas
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    Jenss, Kay Erik
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    Soler, Marc Elias
    Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they allow novel interaction methods and provide insights into user attention, intentions, and activities. However, only few studies have used gaze-enabled AR displays for human activity recognition (HAR). In an experimental study, we collected gaze data from 10 users on a HoloLens 2 (HL2) while they performed three activities (i.e., read, inspect, search). We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved an up to 98.7% activity-recognition accuracy. On the HL2, we provided users with an AR feedback that is relevant to their current activity. We present the components of our system (GEAR) including a novel solution to enable the controlled sharing of collected data. We provide the scripts and anonymized datasets which can be used as teaching material in graduate courses or for reproducing our findings.