Human-Centered Design Science Research Evaluation for Gamified Augmented Reality

Vasilevski, Nikolche; Birt, James R.

doi:10.3389/frvir.2021.713718

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Moreover, the potential to apply deep learning in industrial manufacturing can be used to analyse large amounts of data from sensors and actuators, along with a variety of sources to optimize production processes, improve quality control, and predict the need for maintenance [26]. For instance, a deep learning algorithm can analyse data from sensors to predict when equipment is likely to fail, allowing manufacturers to perform maintenance before a breakdown occurs.…”

Section: Related Workmentioning

confidence: 99%

Non-Fungible Token based Smart Manufacturing to scale Industry 4.0 by using Augmented Reality, Deep Learning and Industrial Internet of Things

Ahmed Khan,

Abubakar Ibrahim

2023

IJPCC

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The recent revolution in Industry 4.0 (IR 4.0) has characterized the integration of advance technologies to bring the fourth industrial revolution to scale the manufacturing landscape. There are different key drivers for this revolution, in this research we have explored the following among them such as, Industrial Internet of Things (IIoT), Deep Learning, Blockchain and Augmented Reality. The emerging concept from blockchain namely “Non-Fungible Token” (NFT) relating to the uniqueness of digital assets has vast potential to be considered for physical assets identification and authentication in the IR 4.0 scenario. Similarly, the data acquired through the deployment of IIoT devices and sensors into smart industry spectrum can be transformed to generated robust analytics for different industry use-cases. The predictive maintenance is a major scenario in which early equipment failure detection using deep learning model on acquired data from IIoT devices has major potential for it. Similarly, the augmented reality can be able to provide real-time visualization within the factory environment to gather real-time insight and analytics from the physical equipment for different purposes. This research initially conducted a survey to analyse the existing developments in these domains of technologies to further widen its horizon for this research. This research developed and deployed a smart contract into an ethereum blockchain environment to simulate the use-case for NFT for physical assets and processes synchronization. The next phase was deploying deep learning algorithms on a dataset having data generated from IIoT devices and sensors. The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. Lastly, the research also proposed an AR based framework for the visualization ecosystem within the industry environment to effectively visualize and monitory IIoT based equipment’s for different industrial use-cases i.e., monitoring, inspection, quality assurance.

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Section: Related Workmentioning

confidence: 99%

Non-Fungible Token based Smart Manufacturing to scale Industry 4.0 by using Augmented Reality, Deep Learning and Industrial Internet of Things

Ahmed Khan,

Abubakar Ibrahim

2023

IJPCC

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“…Tujuannya untuk mendukung pendidik dan pengembang dalam membangun game AR yang imersif. Pada penelitian tersebut, DSRM terbukti menjadi alat yang tak tergantikan untuk merancang dan mengembangkan sebuah solusi untuk masalah kompleks yang muncul di lingkungan yang rumit seperti bidang pendidikan [8]. Pada penelitian sebelumnya, terdapat pula perancangan aplikasi pembelajaran mobile adaptive learning yang diberi nama Nearby Wisdom App [9].…”

Section: Pendahuluanunclassified

Perancangan User Interface dan User Experience Adaptive Mobile Learning Untuk Siswa Sekolah Menengah

Kurnia

Pujiarti

2022

JoSYC

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This study aims to design and analyze the user interface (UI) and user experience (UX) of mobile-based learning (m-learning) applications that implement Adaptive learning. The design method is Design Science Research Methodology (DSRM). A poorly designed user interface can lead to a negative user experience, not using the software properly and continuously. On the other hand, UI can make using software easy and enjoyable, so a well-designed UI of learning apps can produce a pleasant learning experience for students. Adaptive learning is expected to be a method used as a means to balance learning needs to make it more efficient and effective to use. In the Evaluation stage, the User Experience Questionnaire (UEQ) is used which is grouped into six parameters, namely: attractiveness, clarity, Efficiency, accuracy, Stimulation , and Novelty. Based on the results of the Evaluation using UEQ, the mean values obtained for all parameters are in the range of positive values of more than 0.8. The user experience aspect in the form of Attractiveness is in the good category, the clarity aspect is in the above average category, the Efficiency aspect is in the good category, the accuracy is in the above average category, the Stimulation aspect is in the good category, and the Novelty aspect is in the above average category. The implementation of Adaptive learning on the UI and UX of the application has been well received by potential users based on the results of the Demonstration and Evaluation stages.

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“…This evaluation process is iterative in nature and provide us continuous feedback for further improvements. The above-mentioned questionnaire has been developed using XR user experience parameters i.e., learnability, navigability, visibility, satisfaction, usability, and motivation [80,81]. Learnability considers how easy it is for the end-users to interact within XR environment for the first time to become efficient after repetitions.…”

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confidence: 99%

A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study

et al. 2022

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The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway.

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Human-Centered Design Science Research Evaluation for Gamified Augmented Reality

Cited by 5 publications

References 24 publications

Non-Fungible Token based Smart Manufacturing to scale Industry 4.0 by using Augmented Reality, Deep Learning and Industrial Internet of Things

Non-Fungible Token based Smart Manufacturing to scale Industry 4.0 by using Augmented Reality, Deep Learning and Industrial Internet of Things

Perancangan User Interface dan User Experience Adaptive Mobile Learning Untuk Siswa Sekolah Menengah

A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study

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