A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications
| Autor: | Iñigo Fernández del Amo, Demetrius Onoufriou, Rajkumar Roy, John Ahmet Erkoyuncu, Riccardo Palmarini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
QA75
0209 industrial biotechnology Knowledge management General Computer Science Computer science Maintenance Knowledge transfer Context (language use) 02 engineering and technology Context-Awareness 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Context awareness Applied research Adaptation (computer science) Systematic Literature Review Augmented Reality business.industry General Engineering Knowledge capture Systematic review Authoring 020201 artificial intelligence & image processing Augmented reality Interaction-Analysis Thematic analysis business |
| Zdroj: | Computers in Industry |
| Popis: | Augmented Reality (AR) has experienced an increasing trend in applied research in the last few years. This emerging trend is focused in content-related challenges: mainly creation (Authoring), adaptation (Context-Awareness) and improvement (Interaction-Analysis) of augmented content. Research in these techniques has enabled Academia to recognise Augmented Reality capability for knowledge transfer, either from AR systems to users or between users. But to the best of author’s knowledge, there are no specific literature review in these areas, neither on their relations with AR knowledge transfer ability. Therefore, this paper aims to identify these relations through an analysis of state-of-the-art techniques in Authoring (A), Context-Awareness (CA) and Interaction-Analysis (IA) in the context of maintenance applications. In order to do so, a Systematic Literature Review (SLR) has been conducted on 74 application-relevant papers from 2012 to 2017. It comprised a thematic analysis to establish the relation between maintenance applications, research in A, CA and IA and AR knowledge transfer modes. Its results helped to classify AR maintenance-applications by technological readiness levels. They also revealed the potential of AR for users’ knowledge capture, and future research required for full knowledge management capabilities. Furthermore, the SLR method proposed could be extended to correlate AR systems and applications by their knowledge management capabilities in any AR application context. |
| Databáze: | OpenAIRE |
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