Research on context-aware systems has focused on many different areas. This research ranges from application-level implementations to frameworks and context models, and its applications cover various domains. Several models and infrastructures have been developed, which each focus on their own set of attributes and dimensions. Each of these will be discussed below in terms of their overall completeness to fully support the personal user context of a mobile user.
Gehlen, Aijaz, Sajjad, and Walke (2007) introduced a context dissemination middleware based on a mobile Web Services framework. Their mobile context dissemination middleware focused mainly on context attributes including, location, time, task, network, user context and social circumstance. They did not incorporate a health context, which could be seen as part of user context and instead only included a health scenario where their model could be useful.
Falchuk, Loeb, and Panagos, (2008) focused on the challenges of creating middleware that offers rich context-aware event logic to address a spectrum of issues across many verticals. The context attributes that they addressed include:
Personal information (e.g. interests, expertise)
Social information (e.g. contacts, relationship, medical)
Subscriptions (e.g. traffic alerts, various feeds)
Device states
Presence and availability (e.g. willingness to communicate)
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Schedule (e.g. calendar, to-do lists).
Figure 3.1: Classification of user and situation context parameters (Eichler et al. 2009)
Eichler et al., (2009) addressed an approach to service offering and usage on mobile phones. They focused on a typical scenario in public transport and classified their context parameters into semi-static and dynamic contexts, as well as into user and situation specific, as shown in Figure 3.1.
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Eichler et al.'s (2009) situation model highlighted the importance of separating situation specific and user specific contexts to support the right services being provided at the right time based on the current context, as shown in Figure 3.2. Other than the aggregated situation context, the extracted user preferences serve as additional input for an improved adaption of the service offering to the actual requirements of the user. However, the user specific context of the model does not include a health context parameter, which can be seen as a part of a mobile user's personal user context.
Figure 3.3: Situation-aware user model (Fausto & Alberto 2010)
Fausto and Alberto (2010) developed a situation-aware user model to make context-aware mobile services possible and for these services to adapt to changing contexts and user needs. The situation-aware user model sets preferences for the user for a given context and has elements such as social network preferences, situational preferences, physical context and user-activity context, as depicted in Figure 3.3.
Their model suggested that by including social network information as part of context, the user's personal context, metadata of information and recommendations provided to users could be enhanced. This situation-aware user model focused on more context dimensions than Eichler et al.'s (2009) situation model. However, it also does not include the health context of the user.
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Figure 3.4: Context detection service architecture (Christoph, Krempels, Stulpnagel & Terwelp 2010)
Christoph et al. (2010) introduced an integrated approach for the automatic detection of a user’s context. Their Context Detection Service Architecture as shown in Figure 3.4, was however, primarily sensor and behaviour-based and made no reference to a personal user profile or user and situation preferences.
The Context Detection Service Architecture proposes that detection of a mobile user's context should be provided by the mobile device as a service. This would act as an API, so that all applications can have access to the context information and adapt their behaviour accordingly.
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Hassani and Seidl (2011) introduced one of the first methods for predicting an added health context of a mobile user. Unfortunately, the model used required users not only to have a mobile device but also to be equipped with body sensors, which is impractical and could lead to poor user acceptance.
Alidin and Crestani (2012) utilized a “just-in-time” approach, in which the relevant information is retrieved without the user requesting it. They provided more details in terms of how context could be identified and captured but their infrastructure does not support the use of non-sensor data such as calendar and preferences.
Figure 3.6: Four levels interpretation of context model (Alidin & Crestani 2012)
They developed their own context interpretation model in order to translate sensor data into the description of user context. This model consists of four different levels of context interpretation, as depicted in Figure 3.6. This model, however, is too high-level and is missing other user specific context data sources such as user profiles and preferences.
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Figure 3.7: JIT-MobIR conceptual architecture (Alidin & Crestani 2012)
Figure 3.7 helps to clarify the missing data sources in their context interpretation model and separates sensor data from user data. It unfortunately does not incorporate a health context as part of the user data. This architecture does highlight different context retrieval processes that could be used in order to retrieve the user's context, the first being a query-based retrieval and the second a pro-active retrieval.
Figure 3.8 explores the context dimensions that Alidin and Crestani (2012) focused on, which highlight the fact that context attributes also comprise of several states. For example, the location context has three states including: home, workplace and unknown.
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Figure 3.8: General sensors analysis for the context dimensions (Alidin & Crestani 2012)
Reviewing the existing context models and additional literature highlighted that there are many context dimensions and attributes. However, not all dimensions and attributes are seen as relevant in terms of personal mobile user context. Key trends in existing models include the use of preferences as well as separating context information regarding the physical environment from information about the user. From the existing models that were reviewed, the situation-aware model proposed by Fausto and Alberto (2010) appears to be the most complete in comparison to the other models. However, this model does lack several components including a health context dimension as well as context attributes such as device states.