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Human factors related context is structured into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's social environment (co-location of others, social interaction, group dynamics), and the user's tasks (spontaneous activity, engaged tasks, general goals). Likewise, context related to physical environment is structured into three categories: location (absolute position, relative position, [[wikt:colocation|co-location]]), infrastructure (surrounding resources for computation, communication, task performance), and physical conditions (noise, light, pressure, air quality).<ref>[https://link.springer.com/article/10.1007%2Fs11042-010-0711-z?LI=true A Comprehensive Framework for Context-Aware Communication Systems. B. Chihani, E. Bertin, N. Crespi. 15th International Conference on Intelligence in Next Generation Networks (ICIN'11), Berlin, Germany, October 2011]</ref><ref>[https://ieeexplore.ieee.org/document/5956518 A Self-Organization Mechanism for a Cold Chain Monitoring System. C. Nicolas, M. Marot, M. Becker.  73rd Vehicular Technology Conference 2011 IEEE (VTC Spring), Yokohama, Japan May 2011]</ref>

Human factors related context is structured into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's social environment (co-location of others, social interaction, group dynamics), and the user's tasks (spontaneous activity, engaged tasks, general goals). Likewise, context related to physical environment is structured into three categories: location (absolute position, relative position, [[wikt:colocation|co-location]]), infrastructure (surrounding resources for computation, communication, task performance), and physical conditions (noise, light, pressure, air quality).<ref>[https://link.springer.com/article/10.1007%2Fs11042-010-0711-z?LI=true A Comprehensive Framework for Context-Aware Communication Systems. B. Chihani, E. Bertin, N. Crespi. 15th International Conference on Intelligence in Next Generation Networks (ICIN'11), Berlin, Germany, October 2011]</ref><ref>[https://ieeexplore.ieee.org/document/5956518 A Self-Organization Mechanism for a Cold Chain Monitoring System. C. Nicolas, M. Marot, M. Becker.  73rd Vehicular Technology Conference 2011 IEEE (VTC Spring), Yokohama, Japan May 2011]</ref>

和情景相关的人类特征可以被分为3类:用户基本信息（习惯、情绪状态、生物心理状况）、用户社交环境（位置网、社交、群体动态）和用户任务（无意识活动、沉浸式任务、一般任务）。物理环境相关的情景也可以分为3类：位置（绝对位置、相对位置和位置网），设施（周围的计算资源、交流、任务表现）和物理条件（噪音、灯光、压力、空气质量）。

情景相关的人类特征可以被分为3类:用户基本信息（习惯、情绪状态、生物心理状况）、用户社交环境（位置网、社交、群体动态）和用户任务（无意识活动、沉浸式任务、一般任务）。物理环境相关的情景也可以分为3类：位置（绝对位置、相对位置和位置网），设施（周围的计算资源、交流、任务表现）和物理条件（噪音、灯光、压力、空气质量）。

== Relational context : dynamic and non-user-centric definitions==

== Relational context : dynamic and non-user-centric definitions==

Whereas early definitions of context tended to center on users, or devices interfaced directly with users, the oft-cited definition from Dey<ref name="dey2001"/> ("''any information that can be used to characterize the situation of an entity''") could be taken without this restriction. User-centric context, as may be used in the design of [[human-computer interaction|human-computer interfaces]], may also imply an overly clearcut, and partially arbitrary,  separation between "content" (anything which is ''explicitly'' typed in by users, or output to them), and context, which is ''implicit'', and used for [[context adaptation|adaptation]] purposes. A more dynamic and de-centered view, advocated by Dourish <ref> Dourish, Paul. "What we talk about when we talk about context." Personal and ubiquitous computing 8.1 (2004): 19-30.</ref> views context as primarily ''relational''. This was originally congruent with the move from desktop computing to [[ubiquitous computing]], but it does also fit with a broader understanding of [[ambient intelligence]] where the distinctions between ''context'' and ''content'' become relative and dynamic.<ref> https://www.researchgate.net/publication/230704197_Ambient_Intelligence Streitz, Norbert A., and Gilles Privat. "Ambient Intelligence" , ''Universal Access Handbook'' (2009)</ref> In this view, whichever sources of information (such as [[Internet of Things|IoT]] sensors) may be ''context'' for some uses and applications, might also be sources of primary ''content'' for others, and vice versa. What matters is the set of ''relationships'' that link them, together and with their environment. Whereas early descriptions of single-user-centric context could fit with classical [[Entity-attribute-value model|entity-attribute-value models]], more versatile graph-based information models, such as proposed with [[NGSI-LD]], are better adapted to capture the more relational view of context which is relevant for the  [[Internet of Things]], [[Cyber-Physical Systems]] and [[Digital Twins]]. In this broader acceptation, context is not only represented as a set of attributes attached to an entity, it is also captured by a graph that enmeshes this entity with others. Context awareness is the capability to account for this cross-cutting information from different sources.

Whereas early definitions of context tended to center on users, or devices interfaced directly with users, the oft-cited definition from Dey<ref name="dey2001"/> ("''any information that can be used to characterize the situation of an entity''") could be taken without this restriction. User-centric context, as may be used in the design of [[human-computer interaction|human-computer interfaces]], may also imply an overly clearcut, and partially arbitrary,  separation between "content" (anything which is ''explicitly'' typed in by users, or output to them), and context, which is ''implicit'', and used for [[context adaptation|adaptation]] purposes. A more dynamic and de-centered view, advocated by Dourish <ref> Dourish, Paul. "What we talk about when we talk about context." Personal and ubiquitous computing 8.1 (2004): 19-30.</ref> views context as primarily ''relational''. This was originally congruent with the move from desktop computing to [[ubiquitous computing]], but it does also fit with a broader understanding of [[ambient intelligence]] where the distinctions between ''context'' and ''content'' become relative and dynamic.<ref> https://www.researchgate.net/publication/230704197_Ambient_Intelligence Streitz, Norbert A., and Gilles Privat. "Ambient Intelligence" , ''Universal Access Handbook'' (2009)</ref> In this view, whichever sources of information (such as [[Internet of Things|IoT]] sensors) may be ''context'' for some uses and applications, might also be sources of primary ''content'' for others, and vice versa. What matters is the set of ''relationships'' that link them, together and with their environment. Whereas early descriptions of single-user-centric context could fit with classical [[Entity-attribute-value model|entity-attribute-value models]], more versatile graph-based information models, such as proposed with [[NGSI-LD]], are better adapted to capture the more relational view of context which is relevant for the  [[Internet of Things]], [[Cyber-Physical Systems]] and [[Digital Twins]]. In this broader acceptation, context is not only represented as a set of attributes attached to an entity, it is also captured by a graph that enmeshes this entity with others. Context awareness is the capability to account for this cross-cutting information from different sources.

== Applications in situational or social awareness ==

== Applications in situational or social awareness ==

Context awareness has been applied to the area of [[computer-supported cooperative work]] (CSCW) to help individuals work and collaborate more efficiently with each other.  Since the early 1990s, researchers have developed a large number of software and hardware systems that can collect contextual information (e.g., location, video feeds, away status messages) from users.  This information is then openly shared with other users, thereby improving their situational awareness, and allowing them to identify natural opportunities to interact with each other.  In the early days of context-aware computing, many of the systems developed for this purpose were specifically designed to assist businesses or geographically separated work teams collaborate on shared documents or work artifacts.  More recently, however, there has been a growing body of work that demonstrates how this technique can also be applied to groups of friends or family members to help keep them apprised of each other's activities.

Context awareness has been applied to the area of [[computer-supported cooperative work]] (CSCW) to help individuals work and collaborate more efficiently with each other.  Since the early 1990s, researchers have developed a large number of software and hardware systems that can collect contextual information (e.g., location, video feeds, away status messages) from users.  This information is then openly shared with other users, thereby improving their situational awareness, and allowing them to identify natural opportunities to interact with each other.  In the early days of context-aware computing, many of the systems developed for this purpose were specifically designed to assist businesses or geographically separated work teams collaborate on shared documents or work artifacts.  More recently, however, there has been a growing body of work that demonstrates how this technique can also be applied to groups of friends or family members to help keep them apprised of each other's activities.