Now showing 1 - 10 of 118
  • Publication
    NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR System
    (ACM, 2024-05-28)
    Aleksandar Slavuljica
    ;
    ; ;
    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%.
    Type:
    Journal:
    Volume:
    Issue:
  • Publication
    Gaze-enabled activity recognition for augmented reality feedback
    ( 2024-03-16) ; ; ; ;
    Andrew Duchowski
    ;
    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.
    Type:
    Journal:
    Volume:
    Issue:
  • Publication
    The refashion circular design strategy - Changing the way we design and manufacture clothes
    (Elsevier, 2023-06-30)
    Dan, M. Cristina
    ;
    ;
    Approximately 65% of a garment's climate impact stems from fabric production. With 53 M tons of fibre produced every year and 87% ending in landfills, the fashion industry is polluting and wasting precious resources. Therefore, waste prevention and recapturing value become essential. Our research explores the development of a new circular design strategy (CDS)-Refashion, for service innovation through a design thinking process. Grounded in sustainable design strategies and advanced manufacturing technology, the Refashion CDS aims to enable multiple reutilization of fabric before fibre recycling. This is showcased via a proof-of-concept collection launched on the market in 2022, where three pre-designed multifunctional fabric blocks create 11 different garments, accompanied by a diagram showcasing the product-service system's closed-loop material flow.
    Type:
    Journal:
    Volume:
    Scopus© Citations 3
  • Publication
    MR Object Identification and Interaction: Fusing Object Situation Information from Heterogeneous Sources
    (ACM, 2023-09-28) ;
    Khakim Akhunov
    ;
    Federico Carbone
    ;
    ; ; ; ;
    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.
    Type:
    Journal:
    Volume:
    Issue:
    Scopus© Citations 2
  • Publication
    Type:
    Journal:
  • Publication
    MR-FoodCoach: Enabling a convenience store on mixed reality space for healthier purchases
    ( 2022-10-17)
    Ahn, Jaehyun
    ;
    Gaza, Haifa
    ;
    Oh, Jincheol
    ;
    Fuchs, Klaus
    ;
    Wu, Jing
    ;
    ;
    Byun, Jaewook
    Type:
    Journal:
    Scopus© Citations 1
  • Publication
    Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland
    ( 2022-10-24) ;
    Wu, Jing
    ;
    ;
    Fuchs, Klaus
    ;
    Stoll, Melanie
    ;
    Bally, Lia
    The increasingly prevalent diet-related non-communicable diseases (NCDs) constitute a modern health pandemic. Higher nutrition literacy (NL) correlates with healthier diets, which in turn has favorable effects on NCDs. Assessing and classifying people's NL is helpful in tailoring the level of education required for disease self-management/empowerment and adequate treatment strategy selection. With recently introduced regulation in the European Union and beyond, it has become easier to leverage loyalty card data and enrich it with nutrition information about bought products. We present a novel system that utilizes such data to classify individuals into high- and low- NL classes, using well-known machine learning (ML) models, thereby permitting for instance better targeting of educational measures to support the population-level management of NCDs. An online survey (n = 779) was conducted to assess individual NL levels and divide participants into high- and low- NL groups. Our results show that there are significant differences in NL between male and female, as well as between overweight and non-overweight individuals. No significant differences were found for other demographic parameters that were investigated. Next, the loyalty card data of participants (n = 11) was collected from two leading Swiss retailers with the consent of participants and a ML system was trained to predict high or low NL for these individuals. Our best ML model, which utilizes the XGBoost algorithm and monthly aggregated baskets, achieved a Macro-F1-score of .89 at classifying NL. We hence show the feasibility of identifying individual NL levels based on household loyalty card data leveraging ML models, however due to the small sample size, the results need to be further verified with a larger sample size.
    Type:
    Journal:
  • Publication
    A Typology of Automatically Processable Regulation
    (Taylor & Francis, 2022-01-01)
    Guitton, Clement
    ;
    Tamo-Larrieux, Aurelia
    ;
    Type:
    Journal:
    Volume:
    Issue:
  • Publication
    Mapping the Issues of Automated Legal Systems: Why Worry About Automatically Processable Regulation?
    (Springer, 2022-07-04)
    Guitton, Clement
    ;
    Tamo-Larrieux, Aurelia
    ;
    The field of computational law has increasingly moved into the focus of the scientific community, with recent research analysing its issues and risks. In this article, we seek to draw a structured and comprehensive list of societal issues that the deployment of automatically processable regulation could entail. We do this by systematically exploring attributes of the law that are being challenged through its encoding and by taking stock of what issues current projects in this field raise. This article adds to the current literature not only by providing a needed framework to structure arising issues of computational law but also by bridging the gap between theoretical literature and practical implementation. Key findings of this article are: (1) The primary benefit (efficiency vs. accessibility) sought after when encoding law matters with respect to the issues such an endeavor triggers; (2) Specific characteristics of a project—project type, degree of mediation by computers, and potential for divergence of interests—each impact the overall number of societal issues arising from the implementation of automatically processable regulation.
    Type:
    Journal:
  • Publication
    A Step Toward Semantic Content Negotiation
    ( 2022-09-28)
    Taghzouti, Yousouf
    ;
    ; ;
    Content negotiation aims at enabling a server to provide a client with a representation of a resource that meets its needs. However, client and server might desire to negotiate constraints that go beyond the media type or language of the alternative representation. This is especially true in the Semantic Web, as a resource can be described with a single media type, but with different vocabularies (FOAF, schema.org, etc.), and may match specific patterns. In this paper, we propose an approach to increase the flexibility when negotiating a representation between client and server. Our approach follows the goals of the World Wide Web and uses a set of existing technologies: SHACL and profile-based negotiation. We define the mechanism (in terms of protocol and algorithm) for clients to announce their expectations and for servers to react and respond to them. We then explain, through a use case, how the same approach could be used in Web-based Multi-Agent Systems to help autonomous agents achieve their goals on the Web.
    Type:
    Journal: