11,097 research outputs found

    Human-Robot Trust Assessment From Physical Apprehension Signals

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    Neo: Virtual Object Modeling using Commodity Hardware

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    Recent developments in augmented reality technology have paved way for newapplications in a wide range of areas. These include the commercial markets,medicine applications, military applications and education. The technology pro-vides immersive images to enhance our perception of the world. Augmentedreality addresses challenges related to problem-solving by seamlessly integrat-ing digital images into real-world images.In the context of construction and maintenance industry, project inspections canbe time-consuming and tedious. These inspections involve usages of expensiveand specialized hardware. Some inspections even use physical blueprints anddrawings along with standardized measurement tools. This approach can posepractical challenges and be prone to errors.In this thesis we present Neo, a surface reconstruction system on commodityhardware. It utilizes augmented reality technology by scanning physical sur-roundings and reconstructs them as virtual objects. They are displayed on topof the camera’s live preview of the real world. By using a pipeline architecturewe model the physical surroundings in terms of their shapes and visual appear-ances. Cyber-physical information about the reconstructed virtual models areannotated in real-time. Evaluations of the system show us potentials to createrealistic copies of physical object

    Multi-touch Detection and Semantic Response on Non-parametric Rear-projection Surfaces

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    The ability of human beings to physically touch our surroundings has had a profound impact on our daily lives. Young children learn to explore their world by touch; likewise, many simulation and training applications benefit from natural touch interactivity. As a result, modern interfaces supporting touch input are ubiquitous. Typically, such interfaces are implemented on integrated touch-display surfaces with simple geometry that can be mathematically parameterized, such as planar surfaces and spheres; for more complicated non-parametric surfaces, such parameterizations are not available. In this dissertation, we introduce a method for generalizable optical multi-touch detection and semantic response on uninstrumented non-parametric rear-projection surfaces using an infrared-light-based multi-camera multi-projector platform. In this paradigm, touch input allows users to manipulate complex virtual 3D content that is registered to and displayed on a physical 3D object. Detected touches trigger responses with specific semantic meaning in the context of the virtual content, such as animations or audio responses. The broad problem of touch detection and response can be decomposed into three major components: determining if a touch has occurred, determining where a detected touch has occurred, and determining how to respond to a detected touch. Our fundamental contribution is the design and implementation of a relational lookup table architecture that addresses these challenges through the encoding of coordinate relationships among the cameras, the projectors, the physical surface, and the virtual content. Detecting the presence of touch input primarily involves distinguishing between touches (actual contact events) and hovers (near-contact proximity events). We present and evaluate two algorithms for touch detection and localization utilizing the lookup table architecture. One of the algorithms, a bounded plane sweep, is additionally able to estimate hover-surface distances, which we explore for interactions above surfaces. The proposed method is designed to operate with low latency and to be generalizable. We demonstrate touch-based interactions on several physical parametric and non-parametric surfaces, and we evaluate both system accuracy and the accuracy of typical users in touching desired targets on these surfaces. In a formative human-subject study, we examine how touch interactions are used in the context of healthcare and present an exploratory application of this method in patient simulation. A second study highlights the advantages of touch input on content-matched physical surfaces achieved by the proposed approach, such as decreases in induced cognitive load, increases in system usability, and increases in user touch performance. In this experiment, novice users were nearly as accurate when touching targets on a 3D head-shaped surface as when touching targets on a flat surface, and their self-perception of their accuracy was higher

    Augmented reality for computer assisted orthopaedic surgery

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    In recent years, computer-assistance and robotics have established their presence in operating theatres and found success in orthopaedic procedures. Benefits of computer assisted orthopaedic surgery (CAOS) have been thoroughly explored in research, finding improvements in clinical outcomes, through increased control and precision over surgical actions. However, human-computer interaction in CAOS remains an evolving field, through emerging display technologies including augmented reality (AR) – a fused view of the real environment with virtual, computer-generated holograms. Interactions between clinicians and patient-specific data generated during CAOS are limited to basic 2D interactions on touchscreen monitors, potentially creating clutter and cognitive challenges in surgery. Work described in this thesis sought to explore the benefits of AR in CAOS through: an integration between commercially available AR and CAOS systems, creating a novel AR-centric surgical workflow to support various tasks of computer-assisted knee arthroplasty, and three pre–clinical studies exploring the impact of the new AR workflow on both existing and newly proposed quantitative and qualitative performance metrics. Early research focused on cloning the (2D) user-interface of an existing CAOS system onto a virtual AR screen and investigating any resulting impacts on usability and performance. An infrared-based registration system is also presented, describing a protocol for calibrating commercial AR headsets with optical trackers, calculating a spatial transformation between surgical and holographic coordinate frames. The main contribution of this thesis is a novel AR workflow designed to support computer-assisted patellofemoral arthroplasty. The reported workflow provided 3D in-situ holographic guidance for CAOS tasks including patient registration, pre-operative planning, and assisted-cutting. Pre-clinical experimental validation on a commercial system (NAVIO®, Smith & Nephew) for these contributions demonstrates encouraging early-stage results showing successful deployment of AR to CAOS systems, and promising indications that AR can enhance the clinician’s interactions in the future. The thesis concludes with a summary of achievements, corresponding limitations and future research opportunities.Open Acces

    Using Auto-Ordering to Improve Object Transfer between Mobile Devices

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    People frequently form small groups in many social and professional situations: from conference attendees meeting at a coffee break, to siblings gathering at a family barbecue. These ad-hoc gatherings typically form into predictable geometries based on circles or circular arcs (called F-Formations). Because our lives are increasingly stored and represented by data on handheld devices, the desire to be able to share digital objects while in these groupings has increased. Using the relative position in these groups to facilitate file sharing could facilitate intuitive interfaces such as passing or flicking. However, there is no reliable, lightweight, ad-hoc technology for detecting and representing relative locations around a circle. In this thesis, we present three systems that can auto-order locations about a circle based on sensors standard on commodity smartphones. We tested two of these systems using an object passing task in a laboratory environment against unordered and proximity-based systems, and show that our techniques are faster, more accurate, and preferred by users

    Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

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    Background!#!Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.!##!Methods!#!In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback.!##!Results!#!The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7).!##!Conclusion!#!The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development

    Promoting reality awareness in virtual reality through proxemics

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    Head-Mounted Virtual reality (VR) systems provide full-immersive experiences to users and completely isolate them from the outside world, placing them in unsafe situations. Existing research proposed different alert-based solutions to address this. Our work builds on these studies on notification systems for VR environments from a different perspective. We focus on: (i) exploring alert systems to notify VR users about non-immersed bystanders' in socially related, non-critical interaction contexts; (ii) understanding how best to provide awareness of non-immersed bystanders while maintaining presence and immersion within the Virtual Environment(VE). To this end, we developed single and combined alert cues - leveraging proxemics, perception channels, and push/pull approaches and evaluated those via two user studies. Our findings indicate a strong preference towards maintaining immersion and combining audio and visual cues, push and pull notification techniques that evolve dynamically based on proximity
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