The context dimensions identified in Chapter 2, including identity, location, status (user perceived properties such as environmental), time, device and activity, were used as a basis for the building blocks (i.e. dimensions and attributes) of the proposed model. Building upon these dimensions with previous literature in this chapter, a combination of the most relevant and common dimensions and attributes were synthesized. A summary of the most relevant dimensions and examples of associated attributes appears in Table 3.3.
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Table 3.3: A summary of relevant context attributes and dimensions for personal user context
3.7 Conclusion
Mobile smart phones incorporate several sensors that make it possible to capture contextual information to help individuals to better understand the surroundings that affect their daily lives. The most important sensors among these instruments are the accelerometer, GPS and
Context Dimensions
Context Attributes Input Source Available
Location Current location, destination
GPS, Time, Calendar, IPS, Wi-Fi
Yes Time Time, day of week, date Mobile device Yes Activity Walking, running,
driving
Sensors Yes
Schedule Appointments, task list, travel plan
Calendar, To-do list Sometimes Physiological Body temperature,
mood, hunger
Body Sensors No
Identity Age, gender, stereotype User input Yes Interest &
needs
Preferences, history, habits, profile repository
User input, Sensors Yes
Points of interest
Locations of interest Location, Interests, Sensors
Sometimes Social Contacts, relationships Mobile device Sometimes Device States Features, Sensors Mobile device Yes Availability Willingness to
communicate
User input, Sensors Sometimes Environmental Temperature, noise,
light, humidity, forecast
Sensors Yes
Spatial Speed, orientation, acceleration
Accelerometer, compass, gyroscopes.
Yes
Network Selected network, available networks
Wi-Fi, 3G Yes
Subscriptions Traffic alerts, various feeds
Web services Sometimes
Health Conditions, Blood type, Allergies
Personal Health Records (PHRs), user input
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camera. Extracting useful and meaningful high-level user contextual information from low- level smart phone sensor data has not been fully explored. This research gap has provided a new opportunity for mobile applications to leverage user contexts more actively, such as the users' location, activity, social relationship and health status. Some researchers suggest that future developers should not only extract high-level context from raw sensing data but also make an effort to optimise the context solution to support continuous sensing and processing.
Medical health-care can be seen as an important area for research, as it has many critical issues and problems that still need to be addressed. Patients who have chronic conditions need 24/7 monitoring. An important aspect of monitoring patients is the ability to track them. Locating patients can be crucial, especially if they get lost as they move from one location to the next.
Understanding the context that affects health behaviour change and chronic disease management can be very important for developing effective health management practices. Mobile devices can make it possible to capture contextual information such as PHRs to help patients and their healthcare providers. This contextual information will assist them to better understand the circumstances that affect their health and to create strategies to address those trends. Mobile devices, specifically smart phones are becoming an increasingly popular platform for the creation of health interventions. This phenomenon is due to the rapid developments in mobile and wireless technologies.
The results of the application survey conducted by Liu et al. (2011) identified several trends and important implications for application developers. One of these implications was that m- health apps that took advantage of unique smart phone features, such as context awareness, were more popular. This is validated by the trend whereby users gave high ratings to these new, innovative apps such as tracking tools that took advantage of these mobile device features (Liu et al. 2011).
Mobile context modelling is a fundamental research problem with regards to successfully leveraging the rich contextual information of mobile users whilst on the move. Context-aware apps still require a significantly increased context recognition accuracy for high-level context information on inaccurate and imprecise sensor data to enable the robust execution of context-aware apps. Robust context models that capture contextual information can be
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categorized into different dimensions, such as location, time and environments. These dimensions can be seen as the fundamental primitive elements in a context model.
Suggestions that incorporate domain knowledge for common contexts, such as "having dinner" with unsupervised approaches for mobile context modelling could be topics for future research. This semi-supervised approach has the potential to improve the learning performances of common contexts while keeping the flexibility of supervised approaches for learning personalized contexts. Existing context models do not effectively deal with dynamic aspects of contextual information such as location, time, social relationships and changing preferences. These current models lack a suitable design or focus on modelling static aspects of context, but user context is by its very nature highly dynamic.
Eichler et al.'s (2009) situation model highlighted the importance of separating the user and situation specific contexts. It also had a well-structured classification of the context attributes utilized within the situation model. Their model did not, however, include a health context. The situation model by Fausto and Alberto, (2010), also highlighted the separation of user and situation specific contexts, but more importantly it emphasizes the need to have a set of preferences. Alidin and Crestani's (2012) model on the other hand provides an overall picture of context interpretation but lacks specific details in terms of other sources of context including user profile and preference information. The underlying conceptual architecture associated with Alidin and Crestani's (2012) model provides a useful structure whereby sensor data is separated from user data.
These models and infrastructures were considered the top three of those reviewed, with the most complete model being the situation-aware model proposed by Fausto and Alberto (2010). Their model contained the key aspects highlighted from the other two models including separation of situation/physical from user context and an overall picture of the different context dimensions involved. However, their model still lacked several key aspects to successfully model personal user context of a mobile user. Fausto and Alberto's (2010) model together with other additional elements such as an added health context and the context attributes (Table 3.3) can be used as the basis of a new and improved mobile context- aware model.
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This chapter aimed to answer Research Question 2: "What are the problems and requirements of existing context awareness solutions used in mobile applications?" The relevance of mobile devices to support context awareness and mobile context modelling were discussed. Existing mobile context models were reviewed and suitable model/s that can be extended were identified. Context attributes relevant to personal user context to facilitate context awareness in mobile applications were also identified and summarized.
The next chapter will address Research Question 3: "How can an improved context-aware model be developed?" It will also discuss the design and implementation of the proposed context model for a personal user, which will use multiple input sources.
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