Context-based and Rule-based Adaptation of Mobile User Interfaces in mHealth

Alnanih, Reem; Ormandjieva, Olga; Radhakrishnan, T.

doi:10.1016/j.procs.2013.09.051

Cited by 25 publications

(14 citation statements)

References 12 publications

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“…It is interesting to compare the results of this work with those of our previous experimental work carried out for the junior user stereotype [14]. In [14], ten junior doctors with previous experience using the PHIS application at KAUH participated in a controlled experiment conducted in a simulated environment in a university lab, which was different from a real hospital environment.…”

Section: Resultsmentioning

confidence: 99%

“…In [14], ten junior doctors with previous experience using the PHIS application at KAUH participated in a controlled experiment conducted in a simulated environment in a university lab, which was different from a real hospital environment. The connection to the network was a fast one, the environment was not crowded as in a hospital, and the doctors were not under the usual pressures of work.…”

Section: Resultsmentioning

confidence: 99%

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Empirical Evaluation of Intelligent Mobile User Interfaces in Healthcare

Alnanih

Ormandjieva

Radhakrishnan

2014

Advances in Artificial Intelligence

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Abstract.The users of mobile healthcare applications are not all the same, and there may be considerable variation in their requirements. In order for the application to be attractive to potential adopters, the interface should be very convenient and straightforward to use, and easy to learn. One way to accomplish this is with an intelligent mobile user interface (IMUI), so that the application can be readily adapted to suit user preferences. This paper presents the results of adapting the IMUI for the various user stereotypes and contexts in the healthcare environment. We begin with a context model of the healthcare domain and an analysis of user needs, and then proceed to solution analysis, followed by product design, development, and user testing in a real environment. In terms of IMUI design, we focus on adapting the MUI features for users in the healthcare context, either at design time or at runtime.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Empirical Evaluation of Intelligent Mobile User Interfaces in Healthcare

Alnanih

Ormandjieva

Radhakrishnan

2014

Advances in Artificial Intelligence

Self Cite

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“…Hence not only is an MUI a necessary factor in the development process, but also for correctly assessing and implementing for the need of an adaptive MUI. Alnanih et al [17] presents research which informs a proposed context-based and rule-based approach to adaptive MUI design. Such an approach could be considered for use when designing the MUI, as early tests indicate the adaptable system is superior in its effectiveness and task navigation, and in turn reduces the cognitive load of its user.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The mHealth developers should consider the assimilation of either an abstract, context, or adaptive UI design model [17] among others.…”

Section: Ui or Muimentioning

confidence: 99%

A General mHealth Design Pipeline

Vagg

Plant

Tabirca

2016

Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media

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The use of smartphones technologies and apps in health has become increasing popular in recent years and as such has led to the development of the mobile health (mHealth) sector. However, mobile health applications (apps) are often rejected in app stores or not utilised by the intended audience. This has led to much discussion about the review processes, certification, and considerations required to develop a mHealth app for successful deployment. In this paper, we describe a general design pipeline to be used for the creation of mHealth apps. The pipeline considers and makes recommendations for the design of the following in a mHealth app: Preparation, Back-End Development, Front-End Development and Deployment.

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“…In case of the Android platform, for example relatively long pieces of code have to be written for this purpose using Android SDK. Accordingly, many developers limit the number of manipulated sensors and mobile features merely due to the time constraints [2]. What makes the problem worse is that many classes and methods are deprecated over the years [3] entailing learning to use alternative ones.…”

Section: Introductionmentioning

confidence: 99%

Context Management for Supporting Context-aware Android Applications Development

Elazhary

Althubyani

Ahmed

et al. 2017

Int. J. Interact. Mob. Technol.

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Abstract-Building context-aware mobile applications is one of the most ambitious areas of research. Such applications can change their behavior according to context or perform specific tasks in specific contexts. Regardless of the application, all context-aware mobile applications share the need to retrieve and process context information. This paper presents a Context Management tool for the Android platform (ACM). ACM allows easy access to internal onboard mobile sensors and hardware features extracting corresponding raw data. Raw context is processed into higher-level more human-readable context that is provided seamlessly to the mobile applications. Different methods are used for this purpose including fuzzy classifiers. Since different mobiles have different sensors and hardware features, ACM can adapt to the mobile device by deactivating access to unavailable ones. Information regarding the available sensors and hardware features and their specifications can also be queried. Additionally, applications can request notifications regarding context change or specific context values. In addition to providing developers with supporting classes and methods, ACM is accompanied by an application that allows developers to examine its functionality and capabilities before using it. The application can be also used to examine the readings of the different sensors in different situations and thus calibrate them as needed. Additionally, it can be used to modify and personalize default interpretations of raw context values to high-level ones. ACM has been tested empirically and the results show extreme interest of context-aware mobile application developers in its promising capabilities and that it is conducive to facilitating, speeding up and triggering development of many more of such applications.

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Context-based and Rule-based Adaptation of Mobile User Interfaces in mHealth

Cited by 25 publications

References 12 publications

Empirical Evaluation of Intelligent Mobile User Interfaces in Healthcare

Empirical Evaluation of Intelligent Mobile User Interfaces in Healthcare

A General mHealth Design Pipeline

Context Management for Supporting Context-aware Android Applications Development

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