Model-based adaptive user interface based on context and user experience evaluation

Hussain, Jamil; Hassan, Anees Ul; Bilal, Hafiz Syed Muhammad; Ali, Rahman; Afzal, Muhammad; Hussain, Shujaat; Bang, Jae Hun; Baños, Oresti; Lee, Sungyoung

doi:10.1007/s12193-018-0258-2

Cited by 90 publications

(56 citation statements)

References 37 publications

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“…Results show that users are generally satisfied with the tool, which they find simple and easy to use. Tasks involving drawing on the screen can nevertheless be enhanced by using more accurate means than the fingertip plus adaptive user interfaces (Hussain et al 2018). The performed evaluation is limited to healthy people with no recognised cognitive disorder.…”

Section: Discussionmentioning

confidence: 99%

MobileCogniTracker

Wohlfahrt-Laymann

Hermens

Villalonga

et al. 2018

J Ambient Intell Human Comput

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As the population ages, cognitive decline is becoming a worldwide threat to older adults' independence and quality of life.Cognitive decline involves problems with memory, language, thinking and judgement, thus severely compromising multiple aspects of people's everyday life. Diagnosis of cognitive disorders is currently performed through clinical questionnairebased assessments, which are typically conducted by medical experts once symptoms appear. Digital technologies can help providing more immediate, pervasive and seamless assessment, which could, in turn, allow for much earlier diagnosis of cognitive disorders and decline. In this work, we present MobileCogniTracker, a digital tool for facilitating momentary, seamless and ubiquitous clinically-validated cognitive measurements. The proposed tool develops digital cognitive tests in the form of multimedia experience sampling questionnaires, which can run on a smartphone and can be scheduled and assessed remotely. The tool further integrates the digital cognitive experience sampling with passive smartphone sensor data streams that may be used to study the interplay of cognition and physical, social and emotional behaviours. The Mini-Mental State Examination test, a clinical questionnaire extensively used to measure cognitive disorders, has been particularly implemented here to showcase the possibilities offered by our tool. A usability test showed the tool to be usable for performing digital cognitive examinations, and that cognitively unimpaired persons in the relevant age-group are capable of performing such digital examination. A qualitative expert-driven validation also shows a high inter-reliability between the digital and pencil-and-paper version of the test.

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Section: Discussionmentioning

confidence: 99%

MobileCogniTracker

Wohlfahrt-Laymann

Hermens

Villalonga

et al. 2018

J Ambient Intell Human Comput

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“…To resolve this problem, we proposed and applied a re-designed tool that is user-oriented and based on iteration and increments. Hussain et al [6] and Park et al [7] proposed an evaluation principle for user-centered experience for the evaluation of immersive VR experiences or experience operating interface.…”

Section: Introductionmentioning

confidence: 99%

Constructing an Evaluation Model for User Experience in an Unmanned Store

Wang

2019

Sustainability

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Traditional stores feature three characteristics: the goods, convenience, and the service provided to its customers (users). Due to the development of the online/offline omni channel consumption model, the starting point for supplying the user with services is no longer the time at which a user arrives at the store door. Instead, it is expected that services can be merged seamlessly into users’ lives at any point in time. Convenience and quality of service can be maximized and optimized via any medium or device. Therefore, in light of the foreseen commercial requirements of the supply end, we introduce a strategy for implementing intelligent equipment in order to achieve the goals of enhanced efficiency and reduced manpower. We investigate the possibility of traditional stores being replaced by other types of convenient store. This study investigates the experience evaluation of unmanned stores with respect to three dimensions: the economic experience, marketing experience, and qualia experience. A case study approach is implemented in this study. The goal is to investigate the course of the user experience in the X-Store, which was founded by the Uni-President Enterprises Corporation in Taiwan. By determining the relationship between users’ interactions with tangible and intangible objects, it is possible to understand the reasons behind the insufficiency in a bad user experience. It is then possible to deal with the insufficiency represented by an intangible service guidance interface, rather than the single and tangible factor of there being no clerks. Finally, a type I quantification theory is applied to the quantification of qualitative data. It is known that the elements corresponding to higher user ratings include, respectively, entertaining setting, positive sensory experience, and innovative products or facilities. The most representative factors for these elements include an interactive drinks cabinet, a futuristic layout, and facial recognition. In contrast, the elements of lower satisfaction level include a setting far from feelings of hustle and bustle, the experience of being introduced to new ideas (thinking), and facilities that are easy to operate. The most representative factors behind these elements include being unable to perform immersive shopping, there being no memory of limited-edition souvenirs, and apps that are not good to use. The contributions of this study are twofold. Firstly, we provide an evaluation of user experience for the first unmanned store in Taiwan, along with a subsequent ranking of the factors. This could provide companies with a reference for either maintaining or improving upon their current state. Secondly, we analyzed the five-stage experience activities for the embodiment of the interactive relationship between users and other people who were analyzed. Any follow-up changes to user influence can be traced back by means of this approach.

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“…The development frameworks, provided by different OS platform association like android or iOS, suggest the functionalities to read the technical information of the device (e.g. memory, display size and resolution, list of sensors and network connectivity options) [52]. The device properties may include some of the followings: 3) Environmental properties: Environemtal properties presents the information about the surrounding environment like spatio-temporal information.…”

Section: ) User Propertiesmentioning

confidence: 99%

“…Different domains of the mobility environment are experts in mobility infrastructure, service development, device connectivity and interface design, etc. [52]. The mobility of the devices tranfering from one to another environment makes it essential to consider the usage environemt in mobile application interface development.…”

Section: ) User Propertiesmentioning

confidence: 99%

“…Context modeling for usage learning has been defined into three categories such as (i) based on domain knowledge (ii) supervised learning approaches and (iii) unsupervised learning approaches. Supervised learning approaches require smaller amount of domain knowledge while unsupervised learning do not require the domain knowledge [52] [61]. It is very difficult to collect relevant information from users because most of the users are not aware which information is important [41].…”

Section: Usability Engineering For Mobile-phonementioning

confidence: 99%

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Towards Adaptive user Interfaces for Mobile-Phone in Smart World

Iqbal¹,

Nickabadi²,

Shahzad³

et al. 2018

ijacsa

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All applications are developed for context adaptation and provide communication with users through their interfaces. These applications offer new opportunities for developers as well as users by collecting context data and adapting systems behavior accordingly. Particularly, in mobile devices, these mechanisms provide usability increment tremendously. Rigid and non-adaptive interface blocks the features of context awareness. In this paper, we study methods, technologies and criteria which have been proposed specifically for adaptive interfaces. Based on these guidelines, we elaborate the intelligence of adaptivity and usage of context according to user mental model. Further, we have proposed a model to develop user context ontology (UCO) and adaptive interface ontology (AIO) to optimize the use of adaptive mobile interfaces in the context of user preferences. These ontologies organize the perceptions and thoughts of user. The philosophy of User Centered Design (UCD) is proposed to analyze the usability and validity of mobile device interfaces according to user contexts.

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Model-based adaptive user interface based on context and user experience evaluation

Cited by 90 publications

References 37 publications

MobileCogniTracker

MobileCogniTracker

Constructing an Evaluation Model for User Experience in an Unmanned Store

Towards Adaptive user Interfaces for Mobile-Phone in Smart World

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