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CONTEXT OBTAINING METHOD IN SUSTAINABLE WORKPLACES.

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Title: CONTEXT OBTAINING METHOD IN SUSTAINABLE WORKPLACES.
Alternate Title: МЕТОД ВИДОБУВАННЯ КОНТЕКСТУ В СТІЙКИХ РОБОЧИХ ПРИМІЩЕННЯХ.
Authors: Yakubovych, M. I.1 maksym.yakubovych@lnu.edu.ua, Lyashkevych, V. Y.1 vasyl.liashkevych@lnu.edu.ua
Source: Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì. 2026, Vol. 16 Issue 1, p34-42. 9p.
Subjects: Context-aware computing, Cyber physical systems, Energy consumption, Industrial safety, Acquisition of data
Abstract: The modern workplace is rapidly transforming into a complex cyber-physical environment that combines people, technology systems, surroundings, production processes and knowledge. This multidimensionality claims to continuously obtain contextual information, including dynamic information about the status of space, equipment, people and processes, which determines the possibilities for adaptability, security and sustainable management. The paper identifies the role of context as a basic element of adaptive management, reveals the interdisciplinary nature of contextual data, and shows how its correct acquisition affects safety, energy efficiency, productivity and employee well-being. The research methodology includes an analysis of the literature and modern technological solutions, a systematization of context types, the construction of a comparative table of the advantages and disadvantages of existing methods, as well as the use of semantic, expert and simulation validation for a preliminary accelerated assessment. The main results show that each singular method has significant limitations: computer vision (CV) suffers from occlusions, wearable sensors from user unacceptability, digital twins (DTs) from modelling complexity and knowledge graphs (KGs) suffer from high requirements for ontology engineering. The proposed method, based on the hybrid approach, demonstrates the highest accuracy of context obtaining, robustness to data gaps and transparency of solutions based on explained models and semantic integration. The findings show that a combination of physical, semantic and behavioral sources of information provides the most complete picture of the workspace environment states. The proposed context obtaining method integrates heterogeneous data and increases the level of intelligence of workspace management systems. The work contributes to the development of scientific thought in the field of resilience, cyber-physical systems and intelligent monitoring, and also lays the foundation for building adaptive, human-centric decision support systems and automatic microclimate control systems, increasing production incidents and optimizing personnel workload in real workspaces. [ABSTRACT FROM AUTHOR]
Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: CONTEXT OBTAINING METHOD IN SUSTAINABLE WORKPLACES.
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  Data: МЕТОД ВИДОБУВАННЯ КОНТЕКСТУ В СТІЙКИХ РОБОЧИХ ПРИМІЩЕННЯХ.
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  Data: <searchLink fieldCode="DE" term="%22Context-aware+computing%22">Context-aware computing</searchLink><br /><searchLink fieldCode="DE" term="%22Cyber+physical+systems%22">Cyber physical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+safety%22">Industrial safety</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink>
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  Data: The modern workplace is rapidly transforming into a complex cyber-physical environment that combines people, technology systems, surroundings, production processes and knowledge. This multidimensionality claims to continuously obtain contextual information, including dynamic information about the status of space, equipment, people and processes, which determines the possibilities for adaptability, security and sustainable management. The paper identifies the role of context as a basic element of adaptive management, reveals the interdisciplinary nature of contextual data, and shows how its correct acquisition affects safety, energy efficiency, productivity and employee well-being. The research methodology includes an analysis of the literature and modern technological solutions, a systematization of context types, the construction of a comparative table of the advantages and disadvantages of existing methods, as well as the use of semantic, expert and simulation validation for a preliminary accelerated assessment. The main results show that each singular method has significant limitations: computer vision (CV) suffers from occlusions, wearable sensors from user unacceptability, digital twins (DTs) from modelling complexity and knowledge graphs (KGs) suffer from high requirements for ontology engineering. The proposed method, based on the hybrid approach, demonstrates the highest accuracy of context obtaining, robustness to data gaps and transparency of solutions based on explained models and semantic integration. The findings show that a combination of physical, semantic and behavioral sources of information provides the most complete picture of the workspace environment states. The proposed context obtaining method integrates heterogeneous data and increases the level of intelligence of workspace management systems. The work contributes to the development of scientific thought in the field of resilience, cyber-physical systems and intelligent monitoring, and also lays the foundation for building adaptive, human-centric decision support systems and automatic microclimate control systems, increasing production incidents and optimizing personnel workload in real workspaces. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Energy consumption
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      – SubjectFull: Industrial safety
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      – SubjectFull: Acquisition of data
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              Text: 2026
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