User Experience of Markerless Augmented Reality Applications in Cultural Heritage Museums: ‘MuseumEye’ as a Case Study

This paper explores the User Experience (UX) of Augmented Reality applications in museums. UX as a concept is vital to effective visual communication and interpretation in museums, and to enhance usability during a museum tour. In the project ‘MuseumEye’, the augmentations generated were localized based on a hybrid system that combines of (SLAM) markerless tracking technology and the indoor Beacons or Bluetooth Low Energy (BLE). These augmentations include a combination of multimedia content and different levels of visual information that required for museum visitors. Using mobile devices to pilot this application, we developed a UX design model that has the ability to evaluate the user experience and usability of the application. This paper focuses on the multidisciplinary outcomes of the project from both a technical and museological perspective based on public responses. A field evaluation of the AR system was conducted after the UX model considered. Twenty-six participants were recruited in Leeds museum and another twenty participants in the Egyptian museum in Cairo. Results showed positive responses on experiencing the system after adopting the UX design model. This study contributes on synthesizing a UX design model for AR applications to reach the optimum levels of user interaction required that reflects ultimately on the entire museum experience

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Augmented Reality as a Potential Tool for Filmmaking

Augmented Reality (AR) has been used for a wide variety of industries. The purpose of this study was to determine the suitability of this technology for use in filmmaking. One of the problems on a film set is the time taken to block a scene. Blocking involves the placement of subjects and props within a scene. Different ideas have been used for blocking including previzualisation and Virtual Reality (VR). This study proposesed the use of AR as a tool to solve this problem. Marker-based and Markerless AR were assessed in turn to determine their suitability for addressing the problem. The use of AR markers and QR codes were examined in comparison with the use of Simultaneous Localization and Mapping (SLAM) imple mentations. The marker-based AR requires a physical object to scan and markerless is done via the mapping of GPS coordinates. Experiments were conducted on the accu racy and code required for each type of AR. These involved calculating the distances from the marker and the code required to create the virtual content. Surveys and expert interviews were conducted with filmakers and people working in the AR industry to determine the usability and feasibility of the proposed application. This provided a qualitative approach to the technology as the acceptance of any new system is of equal importance to how it functions

Recent Developments and Future Challenges in Medical Mixed Reality

As AR technology matures, we have seen many applicationsemerge in entertainment, education and training. However, the useof AR is not yet common in medical practice, despite the great po-tential of this technology to help not only learning and training inmedicine, but also in assisting diagnosis and surgical guidance. Inthis paper, we present recent trends in the use of AR across all med-ical specialties and identify challenges that must be overcome tonarrow the gap between academic research and practical use of ARin medicine. A database of 1403 relevant research papers publishedover the last two decades has been reviewed by using a novel re-search trend analysis method based on text mining algorithm. Wesemantically identified 10 topics including varies of technologiesand applications based on the non-biased and in-personal cluster-ing results from the Latent Dirichlet Allocatio (LDA) model andanalysed the trend of each topic from 1995 to 2015. The statisticresults reveal a taxonomy that can best describes the developmentof the medical AR research during the two decades. And the trendanalysis provide a higher level of view of how the taxonomy haschanged and where the focus will goes. Finally, based on the valu-able results, we provide a insightful discussion to the current limi-tations, challenges and future directions in the field. Our objectiveis to aid researchers to focus on the application areas in medicalAR that are most needed, as well as providing medical practitioners with latest technology advancements

Ambient Intelligence for Next-Generation AR

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the Springer Handbook of the Metavers

SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average F1 score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.Comment: To appear in Proceedings of IEEE ISMAR 202

Keyframe Tagging: Unambiguous Content Delivery for Augmented Reality Environments

Context: When considering the use of Augmented Reality to provide navigation cues in a completely unknown environment, the content must be delivered into the environment with a repeatable level of accuracy such that the navigation cues can be understood and interpreted correctly by the user. Aims: This thesis aims to investigate whether a still image based reconstruction of an Augmented Reality environment can be used to develop a content delivery system that providers a repeatable level of accuracy for content placement. It will also investigate whether manipulation of the properties of a Spatial Marker object is sufficient to reduce object selection ambiguity in an Augmented Reality environment. Methods: A series of experiments were conducted to test the separate aspects of these aims. Participants were required to use the developed Keyframe Tagging tool to introduce virtual navigation markers into an Augmented Reality environment, and also to identify objects within an Augmented Reality environment that was signposted using different Virtual Spatial Markers. This tested the accuracy and repeatability of content placement of the approach, while also testing participants’ ability to reliably interpret virtual signposts within an Augmented Reality environment. Finally the Keyframe Tagging tool was tested by an expert user against a pre-existing solution to evaluate the time savings offered by this approach against the overall accuracy of content placement. Results: The average accuracy score for content placement across 20 participants was 64%, categorised as “Good” when compared with an expert benchmark result, while no tags were considered “incorrect” and only 8 from 200 tags were considered to have “Poor” accuracy, supporting the Keyframe Tagging approach. In terms of object identification from virtual cues, some of the predicted cognitive links between virtual marker property and target object did not surface, though participants reliably identified the correct objects across several trials. Conclusions: This thesis has demonstrated that accurate content delivery can be achieved through the use of a still image based reconstruction of an Augmented Reality environment. By using the Keyframe Tagging approach, content can be placed quickly and with a sufficient level of accuracy to demonstrate its utility in the scenarios outlined within this thesis. There are some observable limitations to the approach, which are discussed with the proposals for further work in this area