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Heterogeneous Data Fusion for Healthcare Monitoring: A Survey
Published in Rashmi Agrawal, Marcin Paprzycki, Neha Gupta, Big Data, IoT, and Machine Learning, 2020
Shrida Kalamkar, Geetha Mary A
Context awareness is the ability of a system or system component to gather information about its environment at any given time and adapt behaviors accordingly. Contextual or context-aware computing uses software and hardware to automatically collect and analyze data to guide responses. Context information in healthcare applications can help to improve the quality of healthcare delivery, utilise limited healthcare and human resources more efficiently and to better match the healthcare services to the current medical conditions and needs of the patients under health monitoring (Bricon-Souf and Newman, 2007). Albrecht Schmidt et al. (Schmidt, Beigl and Gellersen, no date) state that context can be acquired either explicitly by requiring the users to specify it or implicitly by monitoring the user. So, based on the disease, some features can be identified whose values will help in determining the context. Figure 9.4 shows context feature space.
A Survey of Big Data and Computational Intelligence in Networking
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Yujia Zhu, Yulei Wu, Geyong Min, Albert Zomaya, Fei Hu
As for next generation network management, context awareness is an indispensable feature to obtain smarter systems and ensure that user demands are satisfied in advance regardless of evolving environment. Special infrastructures such as middleware, libraries, toolkits, and frameworks are needed to implement context-aware applications during the development process. Among all the infrastructures, middleware outperforms others in terms of providing plenty of context cycle functionalities including acquisition, modeling, reasoning, and distribution in a regular way with special abstractions. Bilen and Canberk [48] proposed a middleware infrastructure based on binary context tree modeling technique (priority rule and stack data structure are used to represent context attributes in a well-structured and standard format). They implemented this approach in an exemplary smart workplace scenario and showed 12% better time efficiency and 50% more devices connected to the distributed access point.
Internet of Things: A Context-Awareness Perspective
Published in Lu Yan, Yan Zhang, Laurence T. Yang, Huansheng Ning, The Internet of Things, 2008
Davy Preuveneers, Yolande Berbers
Context awareness plays an important role in the aforementioned software architectures to enable services customization according to the current situation with minimal human intervention. Acquiring, analyzing, and interpreting relevant context information [11] regarding the user will be a key ingredient to create a whole new range of smart entertainment and business applications that are more supportive to the user. Although context-aware systems have been in the research epicenter for more than a decade [32,33], the ability to convey and select the most appropriate information to achieve nonintrusive behavior on multiuser-converged service platforms in mobile and heterogeneous environments remains a significant management challenge. Interoperability at the scale of the Internet of Things should go beyond syntactical interfaces and requires the sharing of common semantics across all software architectures. It also demands a seamless integration of existing computational artifacts (hardware and software) and communication infrastructures. Only then can context information be successfully shared between highly adaptive services across heterogeneous devices on large-scale networks that consider this information relevant for their purposes.
Context-Aware Augmented Reality Using Human–Computer Interaction Models
Published in Journal of Control and Decision, 2022
Ying Sun, Qiongqiong Guo, Shumei Zhao, Karthik Chandran, G. Fathima
This paper discussed the intelligent context awareness augmented reality model for reduced user context awareness uncertainty. Context-awareness is the capacity of a device or system component to collect and change its behaviour by contextual. The conceptual calculation for the collection and automated interpretation of software and hardware response management statistics. Context-awareness can educate user applications and improve their experience, including augmented reality and contextual communication. Context-awareness is essential in various applications, including human cognitive semantics, augmented reality, organisational design management. Implementations of context awareness respond to context change through context information. However, this background information may be inaccurate or imprecise and may lead to incorrect adaptation decisions causing issues with the user accessibility of contextual application and impacting the acceptance. Sensor data collected from AR devices describes user behaviours and meaning, then modeled using web standards in an extensible information characteristic and user interaction preference. Hence, in this paper, ICAARM has been proposed to reduce user interaction uncertainty and improve user personalised interaction by analyzing the sensory information based on AR applications about the user's intention to interact with a specific device affordance.
User Preferences in Intelligent Environments
Published in Applied Artificial Intelligence, 2019
Juan Carlos Augusto, Andrés Muñoz
One important driver in the design of Intelligent Environments is the notion of “context” and its associated “context-awareness”. A definition often cited is Dey’s (Dey 2001); however, that definition emphasizes too much the system and travels in the direction from the system to the user. We instead use a person-centric approach (Augusto et al. 2018) which goes from the user to the system so the user determines which the relevant contexts are, that is: “the information which is directly relevant to characterise a situation of interest to the stakeholders of a system”. Context-awareness is then defined as “the ability of a system to use contextual information in order to tailor its services so that they are more useful to the stakeholders because they directly relate to their preferences and needs”. As a result, we believe that user preferences is a dimension which plays an important role in linking users with contexts and hence contributing to these systems being better equipped to identify relevant contexts, to better decide how to act in those contexts, and finally on exhibiting behaviors which are more closely aligned with user’s expectations.
Context-aware system for cardiac condition monitoring and management: a survey
Published in Behaviour & Information Technology, 2022
Godwin Okechukwu Ogbuabor, Juan Carlos Augusto, Ralph Moseley, Aléchia van Wyk
Context-awareness is an important part of systems implemented in areas such as Intelligent Environment, Ambient Intelligence, Pervasive and Ubiquitous Computing (Alegre, Augusto, and Clark 2016). The fundamental idea behind context-awareness in healthcare is to develop a proactive and efficient system, that can correlate patient's contextual information and adapt to the changes in the patient condition and environment (Varshney 2009). A context-aware system is an application that uses contexts to provide vital information or services to the user (Abowd et al. 1999). These contexts could be location, time, identity or activity of the considered subject (Dey 2001). It plays essential role in the healthcare delivery decision-making process and assists physicians to properly and timely monitor patients in their care. Context-aware Decision Support System( DSS) uses context information in problem-solving task to provide service and minimise interaction between computer and human (Chen and Chen 2010). DSS are computer applications developed to assist clinicians in making decisions for patient wellbeing. Such systems range from simple software to complex artificial intelligence applications. It helps in establishing a diagnosis and provides reminder and alert to clinicians when new patterns in a patient's data are discovered (Payne 2000). The most promising applications of this technology in healthcare are for diagnosis of chronic and cardiovascular diseases such as heart disease (Abeledo et al. 2016), Brain disease (Albert et al. 2016), Kidney disease (Ahmad et al. 2017) and Diabetic disease (Kumar, Sharma, and Agarwal 2014), as well as monitoring of the activity of elderly people, using intelligent home monitoring devices (Abeledo et al. 2016; Mighali et al. 2017). It uses context data, in combination with learning algorithms to provide proactive services and highly adaptable context-aware system (Wang and Ahmad 2010).