Abstract Datum: 2023-01-12 Nivå: Kandidatuppsats i Företagsekonomi, 15 högskolepoäng Institution: Akademin för Ekonomi, Samhälle och Teknik, Mälardalens Universitet. Författare: Sonja Blomstermo, Wilma Boberg, Lisa Henriksson  Titel: Hur kan Möbelföretag Arbeta för att Utvecklas med Digitaliseringen? Handledare: Sara Melén Hånell Nyckelord: Detaljhandeln, möbelbranschen, digitalisering, e-handel, showrooms, Augmented Reality (AR), köpintentioner.  Teoretisk Utgångspunkt: The Theory of Planned Behaviour, köpintentioner.  Syfte: Syftet med uppsatsen är att undersöka effekten av digitaliseringen av möbelföretag kopplat till e-handel, AR och showrooms. Studien ämnar att bidra med en ökad kunskap kring hur företag inom möbelbranschen kan arbeta för att anpassa sig till digitaliseringen och uppnå fördelar med hjälp av e-handel, showrooms och AR. Metod: En kvalitativ metod har tillämpats till studien som uppsatsen behandlar. För att göra djupgående analyser av hur två svenska små- och medelstora företag (SME:s) i möbelbranschen har anpassat sig till digitalisering har en kvalitativ fallstudie utförts. Vidare har intervjuer samt observationer inkluderats för att djupare undersöka hur företag inom möbelbranschen kan arbeta med e-handel med tilläggen showrooms och AR för att uppnå fördelar. Empirisk Grund: En observation av ett showroom tillhörande ett möbelföretag, tre observationer av AR-funktionen som ett möbelföretag tillämpat, två intervjuer med anställda på ett möbelföretag, elva intervjuer med konsumenter och sekundärdata.   Slutsats: Denna uppsats har påvisat att showrooms och Augmented Reality som komplement till e-handel inom möbelbranschen kan leda till köpintentioner. Slutsatsen dras att företag i möbelbranschen som enbart bedriver försäljning från e-handel skulle kunna uppnå ytterligare fördelar genom att kombinera AR och showrooms. / Abstract Date: 2023-01-12 Level: Bachelor Thesis in Business Administration, 15 credits Institution: School of Business, Society and Engineering, Mälardalen University Authors: Sonja Blomstermo, Wilma Boberg, Lisa Henriksson Title: How can Furniture Companies Work to Develop with Digitalization?  Supervisor: Sara Melén Hånell Keywords: Retail, the furniture industry, digitalization, e-commerce, showrooms, Augmented Reality (AR), purchase intentions. Theoretical Starting Point: The Theory of Planned Behaviour, purchase intentions. Purpose: The purpose of this essay is to examine the effect of digitalization on furniture companies from the application of e-commerce, AR and showrooms. The essay intends to contribute with an increased knowledge about how companies in the furniture industry can work to adjust to the digitalization and achieve benefits with the help of e-commerce, showrooms and AR.  Method: A qualitative approach has been applied to the study of this essay. In order to conduct in-depth analyses of how two Swedish small- and medium-sized enterprises (SME:s), in the furniture industry have adapted to digitalization, a qualitative case-study research design has been applied. Interviews and observations have also been included to further investigate how companies in the furniture industry can work with e-commerce together with showrooms and AR to obtain advantages.  Empirical Foundation: One observation of a furniture company’s showroom, three observations of a furniture company’s AR feature, two interviews with employees at a furniture company, eleven interviews with consumers as well as secondary data.  Conclusion: This essay has proven that showrooms and Augmented Reality as a complement to e-commerce in the furniture industry can lead to purchase intentions. Companies in the furniture business that solely conducts sales from e-commerce could obtain additional advantages from a combination of AR and showrooms.

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Augmented Reality (AR) and Mixed Reality (MR) have increased in attention recently and there are several implementations in video games and entertainment but also work-related applications. The technology can be used to guide workers in order to do the work faster and reduce human error while performing their tasks.  The potential of this kind of technology is evaluated in this thesis through a proof-of-concept prototype which guides a novice in the kitchen in following a recipe and completing a dish. The thesis shows a comparison between five different object detection algorithms, selecting the best in terms of time performance, energy performance and detection accuracy. Then the selected object detection algorithm is implemented in the prototype application.

In recent years, the extended reality (XR) industry has witnessed remarkable growth, revolutionizing various sectors. The potential of XR to reshape industries and create new business opportunities has captured the attention of entrepreneurs and investors alike, leading to the emergence of numerous early stage businesses venturing into this exciting field. Despite the promising prospects, the XR industry remains in a dynamic and evolving state, presenting both opportunities and challenges for early stage businesses seeking to establish themselves within this competitive landscape. This master thesis aims to explore the experiences of early stage businesses within the extended reality (XR) industry, with the goal of understanding the opportunities and challenges they encounter in the current period of study, spring 2023. The study takes a qualitative research approach, employing observations of early stage XR businesses and semi-structured interviews with professionals within the industry and academia of XR. The analysis is based on thematic analysis applying Disruptive Innovation theory and Adoption Curve Theory combined with Gartner’s Hype Cycle. Together with existing literature the analysis creates a picture of the current opportunities and challenges that exists in the XR field, visualized in a prototype of a 3D mind map. These range from areas such as recruitment, financing and technology development to user adoption, ethics and inclusion. They reflect what businesses are facing in early stages of a relatively new industry as well as some of the political, economical and sociocultural factors. Some of the opportunities are high potential to transform and disrupt industries and markets, creating new ways of interacting in virtual worlds as well as new revenue streams. Some of the challenges are low adoption, funding issues and competition with leading players. Ultimately, the research provides valuable insights of the XR industry which could be used when making strategic decisions for professionals and stakeholders.

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<p>Virtual reality (VR) provides students with a platform to train in a 3D environment where their lab experiments can be replicated. By performing repetitive training, trainees can learn better in the Virtual Environment along with the benefits of the cost-effectiveness. The study focuses on two research questions: 1) Are there differences in the performance and errors committed by subjects in  gamified  vs.  non-gamified  educational  labs?  2)  Does  the  attitude  of  students  matter  in  the  gamified and non-gamified environment? This study examines the difference between gamified and non-gamified training environments' effectiveness in training performance and motivation in the VR training. During this study, the participants were provided with one of the two platforms –gamified and non-gamified and were asked to perform a micro-pipette experiment, where the users used a micropipette to transfer liquid from one container to another. The gamified environment has  the  visual  elements  for  providing  positive  feedback  to  the  users,  and  the  non-gamified environment does not have these elements. During the experiment, the participants’ performance metrics like damage caused to the pipette and time taken to complete the experiment were captured. After the experiment, the participants were asked to answer a questionnaire related to the pipette usage, and then self-report their motivation during the experiment in the VR training. Based on the collected data, it was found that while the participants did seem more motivated in doing the gamified  experiment,  there  were  no  differences  between  the  performance  in  the  gamified  experiment  and  the  non-gamified  experiment.  The  participants  were  however  more  enthusiastic  about  the  gamified  experiment  and  were  even  able  to  provide  suggestions  to  add  more  game  elements. Suggestions for further research and more information on their feedback is also provided. </p>

This project focuses on addressing the challenges faced by manufacturing lines such as complexity and flexibility through the integration of Augmented Reality (AR), Internet of Things (IoT), and Big Data technologies. The objective is to develop a framework that enhances the efficiency, flexibility, and sustainability of manufacturing processes in the context of Industry 4.0.  The project involves the design and implementation of an artifact solution using the UNITY platform. The solution enables users to remotely control and monitor a manufacturing line in real-time through an AR interface. By taking advantage of leveraging IoT devices and sensors, real-time data is collected from the production line, providing valuable insights into performance, maintenance needs, and resource optimization. The collected data is processed and analyzed using Big Data techniques, enabling predictive maintenance, quality control, and optimization of manufacturing processes.  The outcomes of this project will provide valuable insights into the potential of AR, IoT, and Big Data technologies in revolutionizing the manufacturing industry. The artifact solution serves as a proof-of-concept, demonstrating the feasibility and benefits of adopting these technologies for sustainable manufacturing in the context of Industry 4.0. Future research and development can build upon this work to further refine and scale the solution for broader industrial applications.

Bedeutung im Kontext von Schäden erweitert. Es wird eine systematische Methodik für die Schadensidentifikation entwickelt, die die Datenerfassung, Schadensbewertung und Schadensdatenverwaltung umfasst. Für die Erfassung von sichtbaren Schadensdaten wird die semantische Segmentierung verwendet. Zahlreiche Strategien und innovative Convolutional Neural Networks wurden entwickelt, um herkömmliche Netzwerke im Kontext der semantischen Segmentierung zu verbessern. Allerdings wurde ein umfassender Vergleich dieser Netzwerke selten durchgeführt. Für zwei Strategien, der Attention Mechanismen und der Generative Adversarial Networks, wird eine vergleichende Studie durchgeführt, um die semantische Segmentierung zu verbessern. Basierend auf dem U-Net werden neuartige Verteilungstypen für beide Strategien mit verschiedenen Diskriminatoren entwickelt und verglichen. Die am besten abschneidenden Netzwerke werden dann einem Validierungsprozess unterzogen, und auch die kombinierten Effekte der beiden Strategien werden vertieft untersucht. Die Cloud Simulation wird zur numerischen Bewertung von Brückenschäden und der Identifikation von verdeckten Schäden angewendet. Zwei Ansätze, nämlich der Single Variation Approach (SVA) und der Dual Variation Approach (DVA), werden vorgestellt. Beide Ansätze werden auf unterschiedliche Szenarien angewendet, um den Einfluss verschiedener Lastfälle und Überwachungspunkte zu studieren. Der effektivere DVA-Ansatz wird in einem Prototyp implementiert, der Funktionalitäten wie Datenkonvertierung, Visualisierung, Generierung von Modellvariationen und Ergebnisanalyse umfasst. Zur Validierung wird eine Stahlbetonbrücke analysiert. Qualitative und quantitative Bewertungen für die Schadensrehabilitation werden in eine Wissensbasis integriert, die automatische Vorschläge für die praktische Schadensrehabilitation für die infizierten Schäden liefert. Augmented Reality wird zur Verbesserung des Visualisierungsergebnisses bei der Schadensinformationsverwaltung für die Vor-Ort-Inspektion und die Rehabilitationsinformationen eingesetzt und in einer Baustellenumgebung validiert. Den Abschluss bildet eine Marketingperspektive der neugewonnenen Ergebnisse. / The entire research work is dedicated to reinterpreting the concept of system identification by exploring its connotation and expanding its denotation in the context of damage. The work focuses on bridges as representative structures and proposes a systematic methodology for damage identification, encompassing damage data acquisition, damage assessment, and damage data management.  Semantic segmentation is employed for viewable damage data identification. Numerous strategies and innovative convolutional neural networks have been developed to enhance traditional networks in the context of semantic segmentation. However, a comprehensive comparison of these networks has been rarely conducted. Two strategies, namely attention mechanisms and generative adversarial networks, are examined in order to enhance semantic segmentation. Based on the U-net, novel distribution types of attention mechanisms and generative adversarial networks with different discriminators are compared in a lightweight test. The best performed networks are then implemented in the validation process, and in addition the combined effects of the attention mechanism and discriminator are investigated. Cloud simulation is applied for quantitative evaluation and identification of non-viewable damage. Two approaches, namely the Single Variation Approach (SVA) and the Dual Variation Approach (DVA), are introduced and applied to different scenarios to account for various load cases and monitoring points as variables. A prototype is developed to implement the more effective DVA approach, incorporating functionalities such as data conversion, visualization, model variation generation and result analysis. A monitored concrete bridge is employed for validation of the assessment of the effectiveness and reliability of the method. Qualitative and quantitative assessments are incorporated into a knowledge base for damage rehabilitation, which automatically provide practical suggestion for the specific identified damage.  Augmented reality is utilized to enhance the visualization experience for on-site inspection providing rehabilitation information and a prototype in a construction setting. The conclusion presents a marketing perspective on the findings.

Humans are very good at conveying when something is lost or misinterpreted in communication by using social cues like facial expressions or changes in prosody. However, these methods are usually not applicable for most robots, which are appearance constrained and vocality constrained. This is also one of the key factors that restrain the efficiency of Human-Robot Interaction (HRI). In this project, we explore a novel paradigm for enhancing the perception of the robot’s internal states using augmented reality (AR). A series of visualization interfaces augmenting either the environment, robot, or target object are implemented and evaluated through a user study. We found that AR visualization improved efficiency and motion predictability over a control group in which there is no visualization. The project shows not only the potential of AR visualizing as a bridge coordinating human and robot, but also a promising future of applications visualising robot’s internal states. / Människor är mycket bra på att förmedla när något går förlorat eller tolkas felaktigt i kommunikationen genom att använda sociala ledtrådar som ansiktsuttryck eller förändringar i prosodi. Dessa metoder är dock vanligtvis inte tillämpliga för de flesta robotar, vilka är begränsade till utseendet och begränsad vokalitet. Detta är också en av nyckelfaktorerna som begränsar effektiviteten i Human-Robot Interaction (HRI). I detta projekt utforskar vi ett nytt paradigm för att förbättra uppfattningen om robotens interna tillstånd med hjälp av augmented reality (AR). En serie visualiseringsgränssnitt som förstärker antingen miljön, roboten eller målobjektet implementeras och utvärderas genom en användarstudie. Vi fann att AR-visualisering förbättrade effektiviteten och rörelsesförutsägbarheten över en kontrollgrupp där det inte finns någon visualisering. Projektet visar inte bara potentialen för AR- visualisering som en bro som koordinerar människa och robot utan också en lovande framtid för applikationer som visualiserar robotens interna tillstånd.

Eye-tracking technology is used within various fields of research such as medicine, computer vision and psychology among others. Despite the many application areas and a growing interest, most eye-trackers rely on dedicated and expensive hardware to track eye movements on large monitors such as desktop displays. Eye-tracking on mobile devices using their built-in camera would be beneficial on a large scale since additional, expensive, equipment would not be needed. Because of the many smartphone users, the technology would be more available. This study aimed to explore the possibilities of using a smartphone camera and augmented reality (AR) for eye-tracking, as well as evaluate the accuracy and precision such a system could obtain. The methods used for gaze estimation were a ray-plane intersection and a system-controlled calibration using linear scaling and translation to adjust the future gaze positions. The mobile device eye-tracker was evaluated in terms of accuracy and precision by two validations using a point pattern to collect gaze data on fixations detected by the system. The result showed an accuracy and precision below optimal compared to previous studies on mobile eye- trackers and eye-trackers that use infra-red light to track the eye-movements. Several factors could have affected the result such as the environments in which the evaluations were held, eye-blinks during data collection or the implementation of the eye-tracking system including the method for calibration. Future work is recommended to evaluate the system in a controlled lab environment, including a larger number of participants and examine if the accuracy is mainly affected by external factors or if it is affected by the implementation of the system. Because of the many benefits of mobile device eye- tracking, future work within the area is encouraged. It would be interesting to investigate the ability to use a mobile device eye-tracker with unrestricted head- and hands movement for further use-cases and a more natural experience. / Teknik för ögonspårning används inom olika forskningsområden såsom medicin, datorsyn och psykologi. Trots många användningsområden och ett växande intresse förlitar sig de flesta ögonspårare på dyr hårdvara för att spåra ögonrörelser på stora bildskärmar. Ögonspårning på mobila enheter med hjälp av inbyggd kamera skulle vara fördelaktigt i stor skala eftersom ytterligare, dyr utrustning inte skulle behövas. Med tanke på de många mobiltelefonanvändare skulle tekniken vara mer tillgänglig. Denna studie syftade till att undersöka möjligheterna att använda en mobiltelefons kamera och förstärkt verklighet (AR) för ögonspårning, samt utvärdera noggrannheten och precisionen som ett sådant system skulle kunna uppnå. Metoderna som användes för att estimera blickens position var en strålplanskorsning och en systemstyrd kalibrering med linjär skalning och förskjutning för att justera framtida blickpositioner. Den mobila enhetens ögonspårare utvärderades i termer av noggrannhet och precision genom två valideringar med hjälp av ett punktmönster för att samla in blickdata från fixeringar som upptäcks av systemet. Resultatet visade en noggrannhet och precision under optimal jämfört med tidigare studier på mobila ögonspårare och ögonspårare som använder infrarött ljus. Flera faktorer kan ha påverkat resultatet, till exempel de miljöer i vilka utvärderingarna gjordes, blinkningar under datainsamlingen eller implementeringen av systemet inklusive val av kalibreringsmetod. Framtida arbete rekommenderas att utvärdera systemet i en kontrollerad laboratoriemiljö med ett större antal deltagare för att undersöka ifall noggrannheten huvudsakligen påverkas av externa faktorer eller av implementeringen av systemet. På grund av de många fördelarna med ögonspårning på mobila enheter uppmuntras framtida arbete inom området.