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Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications

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Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications

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dc.contributor.author Izquierdo-Doménech, Juan es_ES
dc.contributor.author Linares-Pellicer, Jordi es_ES
dc.contributor.author Orta-López, Jorge es_ES
dc.date.accessioned 2024-04-11T06:28:32Z
dc.date.available 2024-04-11T06:28:32Z
dc.date.issued 2023-04 es_ES
dc.identifier.issn 1380-7501 es_ES
dc.identifier.uri http://hdl.handle.net/10251/203289
dc.description.abstract [EN] With its various available frameworks and possible devices, augmented reality is a proven useful tool in various industrial processes such as maintenance, repairing, training, reconfiguration, and even monitoring tasks of production lines in large factories. Despite its advantages, augmented reality still does not usually give meaning to the elements it complements, staying in a physical or geometric layer of its environment and without providing information that may be of great interest to industrial operators in carrying out their work. An expert¿s remote human assistance is becoming an exciting complement in these environments, but this is expensive or even impossible in many cases. This paper shows how a machine learning semantic layer can complement augmented reality solutions in the industry by providing an intelligent layer, sometimes even beyond some expert¿s skills. This layer, using state-of-the-art models, can provide visual validation and new inputs, natural language interaction, and automatic anomaly detection. All this new level of semantic context can be integrated into almost any current augmented reality system, improving the operator¿s job with additional contextual information, new multimodal interaction and validation, increasing their work comfort, operational times, and security. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Multimedia Tools and Applications es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Augmented reality es_ES
dc.subject Semantics es_ES
dc.subject Deep learning es_ES
dc.subject Industry es_ES
dc.subject CNN es_ES
dc.subject Transformers es_ES
dc.subject Multimodal interaction es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11042-022-13803-1 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi es_ES
dc.description.bibliographicCitation Izquierdo-Doménech, J.; Linares-Pellicer, J.; Orta-López, J. (2023). Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications. Multimedia Tools and Applications. 82(10):15875-15901. https://doi.org/10.1007/s11042-022-13803-1 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11042-022-13803-1 es_ES
dc.description.upvformatpinicio 15875 es_ES
dc.description.upvformatpfin 15901 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 82 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\471935 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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