Extended reality solutions are becoming increasingly relevant for companies to implementand use commercially. One sector that have shown great potential and can benefit from implementing this type of technology is the healthcare industry. More specifically, for solutions connected to medical devices. Meanwhile, the healthcare industry has generally been particularly slow at adopting new technologies. Digitalization has been observed to progress at a slower pace than in other sectors. This study investigated why technology is progressing at slow pace and identified critical drivers and bottlenecks that affect the implementation of extended reality within healthcare. This was done through a case study on a proof concept project where the value of a mixed reality solution in the healthcare sector was investigated. This was particularly interesting as the tested solution was the first of its kind in Sweden. The results of the study revealed several barriers within the healthcare sector, including conservatism in the industry, lack of incentives, lack of absorption as well as data protection and privacy issues. Furthermore, theresults revealed that implementation of XR solutions into Swedish healthcare is in first phase. However, underlying drivers imply that there is value in further development. Especially, since the proof of concept was considered successful and showed signs of progressing to the next stage of implementation.

<p>Immersive reality technology, such as augmented and virtual reality, has recently become quite prevalent due to innovation in hardware and software, leading to cheaper devices such as Head-mounted displays. There is significant evidence of an improved rate of skill acquisition with immersive reality training. However, the knowledge required to develop content for such immersive media is still relatively high. Subject experts often work together with programmers to create such content. </p> <p><br></p> <p>Our research goal in this thesis can be broadly classified into four distinct but mutually dependent categories. First, we explored the problem of immersive content creation with ProcessAR, an AR-based system to develop 2D/3D content that captures subject matter experts (SMEs) environment-object interactions in situ. The design space for ProcessAR was identified from formative interviews with AR programming experts and SMEs, alongside a comparative design study with SMEs and novice users. To enable smooth workflows, ProcessAR locates and identifies different tools/objects through computer vision within the workspace when the author looks at them. We explored additional features, such as embedding 2D videos with detected objects and user-adaptive triggers. A final user evaluation comparing ProcessAR and a baseline AR authoring environment showed that, according to our qualitative questionnaire, users preferred ProcessAR.</p> <p><br></p> <p>Second, we explored a unified authoring and editing environment, EditAR, that can create content for multiple media, such as AR, VR, and video instructions, based on a single demonstration. EditAR captures the user's interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with the subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings via a 3D editor, which uses freehand interaction for in-headset editing. Users took five times less time to record instructions and preferred EditAR, giving significantly higher usability scores.</p> <p><br></p> <p>We then explore AnnotateXR, an extended reality (XR) workflow to collect various high fidelity data and auto-annotate it in a single demonstration. AnnotateXR allows users to align virtual models over physical objects, tracked with 6DoF sensors. AnnotateXR utilizes a hand tracking capable XR HMD coupled with 6DoF information and collision detection to enable algorithmic segmentation of different actions in videos through its digital twin. The virtual-physical mapping provides a tight bounding volume to generate semantic segmentation masks for the captured image data. Alongside supporting object and action segmentation, we also support other dimensions of annotation required by modern CV, such as Human-Object, Object-Object, and rich 3D recordings, all with a single demonstration. Our user study shows AnnotateXR produced over 112,000 annotated data points in 67 minutes while maintaining the same performance quality as manual annotations.</p> <p><br></p> <p>Lastly, We conducted two elicitation studies empirically evaluated to determine design guidance for cross-modal devices capable of supporting an immersive interface in VR, allowing for simultaneous interaction with direct hand interaction while allowing for keyboard and mouse input. Recent advances in hand tracking have allowed users to interact with and experience interactions closer and similar to interactions in the physical world. However, these added benefits of natural interaction come at the cost of precision and accuracy offered by legacy input media such as a keyboard/mouse. The results and the guidelines from the two studies were used to develop a prototype called the Immersive Keyboard, which was evaluated against only traditional interface of only the keyboard and mouse. </p> <p><br></p> <p>In this thesis, we have explored a novel extended reality authoring environment that enables users without programming to author asynchronous immersive content in-situ, especially for spatial instructions.</p>

The emergence of 6G and beyond networks is set to enable a range of novel services such as personalized highly immersive experiences, holographic teleportation, and human-like intelligent robotic applications. Such applications require a set of stringent sensing, communication, control, and intelligence requirements that mandate a leap in the design, analysis, and optimization of today's wireless networks. First, from a wireless communication standpoint, future 6G applications necessitate extreme requirements in terms of bidirectional data rates, near-zero latency, synchronization, and jitter. Concurrently, such services also need a sensing functionality to track, localize, and sense their environment. Owing to its abundant bandwidth, one may naturally resort to terahertz (THz) frequency bands (0.1 − 10 THz) so as to provide significant wireless capacity gains and enable high-resolution environment sensing. Nonetheless, operating a wireless system at the THz band is constrained by a very uncertain channel which brings forth novel challenges. In essence, these channel limitations lead to unreliable intermittent links ergo the short communication range and the high susceptibility to blockage and molecular absorption. Second, given that emerging wireless services are "intelligence-centric", today's communication links must be transformed from a mere bit-pipe into a brain-like reasoning system. Towards this end, one can exploit the concept of semantic communications, a revolutionary paradigm that promises to transform radio nodes into intelligent agents that can extract the underlying meaning (semantics) or significance in a data stream. However, to date, there has been a lack in holistic, fundamental, and scalable frameworks for building next-generation semantic communication networks based on rigorous and well-defined technical foundations. Henceforth, to panoramically develop the fully-fledged theoretical foundations of future 6G applications and guarantee affluent corresponding experiences, this dissertation thoroughly investigates two thrusts. The first thrust focuses on developing the analytical foundations of THz systems with a focus on network design, performance analysis, and system optimization. First, a novel and holistic vision that articulates the unique role of THz in 6G systems is proposed. This vision exposes the solutions and milestones necessary to unleash THz's true potential in next-generation wireless systems. Then, given that extended reality (XR) will be a staple application of 6G systems, a novel risk and tail-based performance analysis is proposed to evaluate the instantaneous performance of THz bands for specific ultimate virtual reality (VR) services. Here, the results showcase that abundant bandwidth and the molecular absorption effect have only a secondary effect on the reliability compared to the availability of line-of-sight. More importantly, the results highlight that average metrics overlook extreme events and tend to provide false positive performance guarantees. To address the identified challenges of THz systems, a risk-oriented learning-based design that exploits reconfigurable intelligent surfaces (RISs) is proposed so as to optimize the instantaneous reliability. Furthermore, the analytical results are extended to investigate the uplink freshness of augmented reality (AR) services. Here, a novel ruin-based performance is conducted that scrutinizes the peak age of information (PAoI) during extreme events. Next, a novel joint sensing, communication, and artificial intelligence (AI) framework is developed to turn every THz communication link failure into a sensing opportunity, with application to digital world experiences with XR. This framework enables the use of the same waveform, spectrum, and hardware for both sensing and communication functionalities. Furthermore, this sensing input is intelligently processed via a novel joint imputation and forecasting system that is designed via non-autoregressive and transformed-based generative AI tools. This joint system enables fine-graining the sensing input to smaller time slots, predicting missing values, and fore- casting sensing and environmental information about future XR user behavior. Then, a novel joint quality of personal experience (QoPE)-centric and sensing-driven optimization is formulated and solved via deep hysteretic multi-agent reinforcement learning tools. Essentially, this dissertation establishes a solid foundation for the future deployment of THz frequencies in next-generation wireless networks through the proposal of a comprehensive set of principles that draw on the theories of tail and risk, joint sensing and communication designs, and novel AI frameworks. By adopting a multi-faceted approach, this work contributes significantly to the understanding and practical implementation of THz technology, paving the way for its integration into a wide range of applications that demand high reliability, resilience, and an immersive user experience. In the second thrust of this dissertation, the very first theoretical foundations of semantic communication and AI-native wireless networks are developed. In particular, a rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from AI, causal reasoning, transfer learning, and minimum description length theory is proposed. Within this framework, the dissertation demonstrates that moving from data-driven intelligence towards reasoning-driven intelligence requires identifying association (statistical) and causal logic. Additionally, to evaluate the performance of semantic communication networks, novel key performance indicators metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the imminent convergence of computing and communication resources. Then, a novel contrastive learning framework is proposed so as to disentangle learnable and memoizable patterns in source data and make the data "semantic-ready". Through the development of a rigorous end-to-end semantic communication network founded on novel concepts from communication theory and AI, along with the proposal of novel performance metrics, this dissertation lays a solid foundation for the advancement of reasoning-driven intelligence in the field of wireless communication and paves the way for a wide range of future applications. Ultimately, the various analytical foundations presented in this dissertation will provide key guidelines that guarantee seamless experiences in future 6G applications, enable a successful deployment of THz wireless systems as a versatile band for integrated communication and sensing, and build future AI-native semantic communication networks. / Doctor of Philosophy / To date, the evolution of wireless networks has been driven by a chase for data rates, i.e., higher download or upload speeds. Nonetheless, future 6G applications (the generation succeeding today's fifth generation 5G), such as the metaverse, extended reality (encompassing augmented, mixed, and virtual reality), and fully autonomous robots and vehicles, necessitate a major leap in the design and functionality of a wireless network. Firstly, wireless networks must be able to perform functionalities that go beyond communications, encompassing control, sensing, and localization. Such functionalities enable a wide range of tasks such as remotely controlling a device, or tracking a mobile equipment with high precision. Secondly, wireless networks must be able to deliver experiences (e.g. provide the user a sense of immersion in a virtual world), in contrast to a mere service. To do so, extreme requirements in terms of data rate, latency, reliability, and sensing resolution must be met. Thirdly, intelligence must be native to wireless networks, which means that they must possess cognitive and reasoning abilities that enable them to think, act, and communicate like human beings. In this dissertation, the three aforementioned key enablers of future 6G experiences are examined. Essentially, one of the focuses of this dissertation is the design, analysis, and optimization of wireless networks operating at the so-called terahertz (THz) frequency band. The THz band is a quasi-optical (close to the visible light spectrum) frequency band that can enable wireless networks to potentially provide the extreme speeds needed (in terms of communications) and the high-resolution sensing. However, such frequency bands tend to be very susceptible to obstacles, humidity, and many other weather conditions. Therefore, this dissertation investigates the potential of such bands in meeting the demands of future 6G applications. Furthermore, novel solutions, enablers, and optimization frameworks are investigated to facilitate the successful deployment of this frequency band. To provide wireless networks with their reasoning ability, this dissertation comprehensively investigates the concept of semantic communications. In contrast to today's traditional communication frameworks that convert our data to binary bits (ones and zeros), semantic communication's goal is to enable networks to communicate meaning (semantics). To successfully engineer and deploy such networks, this dissertation proposes a novel suite of communication theoretic tools and key performance indicators. Subsequently, this dissertation proposes and analyzes a set of novel artificial intelligence (AI) tools that enable wireless networks to be equipped with the aforementioned cognitive and reasoning abilities. The outcomes of this dissertation have the potential to transform the way we interact with technology by catalyzing the deployment of holographic societies, revolutionizing the healthcare via remote augmented surgery, and facilitating the deployment of autonomous vehicles for a safer and more efficient transportation system. Additionally, the advancements in wireless networks and artificial intelligence proposed in this dissertation could also have a significant impact on various other industries, such as manufacturing, education, and defense, by enabling more efficient and intelligent systems. Ultimately, the societal impact of this research is far-reaching and could contribute to creating a more connected and advanced world.

Background: There is a noticeable digital shift in the fashion industry. COVID-19 Pandemic has acted as a great accelerator to the digital shift. Specifically retail is one of the industries that has grown more digitally oriented as a result of the global pandemic that hit the world in 2020 (Moneta &amp; Sinclair, 2020). Extended reality technologies (XR) are major components of modern technologies. VR and AR, that fall under XR are being incorporated into the fashion media ecosystem, with the use of different applications (Silvestri, 2020). With the help of XR technology, virtual clothing was introduced to the consumers, which is the intersection of fashion and technology.  Purpose: In order to fill the gaps of literature, the purpose of this paper is to examine how young adults are adopting the brand’s use of extended reality technologies (XR), but specifically, their willingness to adopt virtual clothing.  Method: A qualitative approach was used which followed an interpretivist research philosophy. It included in-depth semi-structured interviews with 12 participants aged between 20-35. The general analytical approach was used to analyze the collected data, and the empirical findings were discussed in relation to the frame of reference.  Conclusion: The findings suggest young adults' adoption to brands' use of extended realities, specifically Virtual clothing is limited due to the lack of knowledge about those technologies. However there are clear signs indicating that they would adopt it in the future if it becomes a norm.