Augmented reality situated visualization in decision-making

Martins, Nardênio Almeida; Marques, Bernardo; Alves, João; Araújo, Tiago; Dias, Paulo; Santos, Beatriz Sousa

doi:10.1007/s11042-021-10971-4

Cited by 46 publications

(20 citation statements)

References 52 publications

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“…Works focused on comparing traditional and AR-based learning are [ 219 , 220 ]. The discussion of AR technologies’ implementation possibility in decision-support processes is presented in [ 221 ].…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, 4) Maintenance system architecture, and 5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

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Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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“…Visualization methods and augmented reality (AR) are also becoming more sophisticated thanks to advances in AI. What is essential, however, is that these technologies are now very widespread and affordable, and are therefore used in a wide variety of application situations as tools for decision support [177]. Domain experts can be provided with contextual and relevant information in an unobtrusive way (AR glasses for forestry technology).…”

Section: Augmentation Explanation and Verification Technologiesmentioning

confidence: 99%

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

Holzinger

Saranti

Angerschmid

et al. 2022

Sensors

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The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.

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“…Augmented reality technology is a technology that makes it easy for users to make artificial visualizations of the natural world (Martins et al, 2022) through scans in specific areas through smartphone assistive media (Köhler et al, 2019).Unlike the case with virtual reality technology, which provides an opportunity for users to observe an artificial environment through the media of VR glasses (Panagiotidis, 2021). Augmented reality technology allows students to see virtual 3D objects that have been developed and added to the natural environment (A.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Utilization of Augmented Reality Media in Vocational Education in Diy Using K-Means

Pratama

Triyono²,

Hasanah³

et al. 2022

jpensil

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The purpose of this study is to reveal group data on the use of augmented reality media in teaching and learning activities so that it is easier to improve student learning outcomes. Augmented reality media was chosen based on the complexity of the scope of teaching materials consisting of thinking skills and limited practice during the implementation of distance learning. The approach for grouping the use of augmented reality media is through the K-Means algorithm with R software. The sample of this study was 136 students of Building Information and Modeling Design skills competency from 5 State Vocational High Schools spread across districts and cities, DIY Province. The review aspect used as the basis for grouping is the need for alternative media in augmented reality, ease of use, supporting facilities, and speed of understanding the material. The grouping results explain that as many as 80 students can master the material well through augmented reality media, and it is appropriate to use it during distance learning. In comparison, the remaining 56 students still find it difficult. Through the results of this grouping, it can be used to combine other media for students who still find it difficult when participating in distance learning

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Augmented reality situated visualization in decision-making

Cited by 46 publications

References 52 publications

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

Mapping the Utilization of Augmented Reality Media in Vocational Education in Diy Using K-Means

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