A Study On Adaptive User Interfaces Computer Science Essay

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Adaptive computing focuses on the methods of adapting a system according to the users requirements. The aim of an adaptive approach is to provide good visibility and mode of adaptation of the system according to the user specifications. To provide transparency about the adaptation the user's mental model is embedded in the design approach. The process of embedding the user model in the design approach gives good visibility about the adaptation process of the system according to the user requirement.

The adaptation process reduces the computation pressure on the hardware devices. The adaptation can be done to reduce the pressure on the micro-processors and reduce the power consumption as well. The method of recognizing the hotspots or code regions in the program, with usage of dynamic optimizers we can find the shortest path to accomplish the user task.

Integrating the models for adaptive and intelligent human computer interaction includes the collection of data from a specific set of user's. The data also includes the gestures and nonverbal data of the user and both are integrated to form a system that adapt to user requirement. The adaptation is decided by the system architecture of adaptive and IHCI (Intelligent Human Computer Interaction).

There are two techniques used in adaptive user interfaces: adaptive presentation and adaptive navigation which make implementation of user tasks efficient. In order to present the user with graphics from the web there must be an interoperable system to adjust to the user's graphical requirements so that even the visually impaired user can use the web efficiently. The low performance and awkward interfaces are eliminated in mobile devices by using adaptive user interfaces which are incorporated in the mobile devices.


Services supporting current adaptive interfaces are very much limited and focus on a few situations. A group of user requirements rather than individual what is taken into account for adaptation process which is called Collaborative work activities should be adapted. The next generation of user's adaptive interfaces is context-aware systems which are mainly built on user centered design.

The adaptive systems can extend to various fields in the real world. They are useful in simple applications as well as in complex applications such as defense research, computational research etc..,

Design Approach to Adaptive Systems

There are many methods to make the system adaptive to the user requirements. System can be adapted by the user model or through the automated systems as said in the management of the adaptive computing environment. Adaptive Computing Environment is equipped with many components which decide the shortest path for achieving the user task.

An adaptive computing focuses on adaptation of the interface according to the characteristics of the user. The adaptation requires a mental model for the user to be in built in the system; it gives a clear idea of the adaptation process to the user according to his requirements.

The information can be collected from the user through the question and answer sessions, where the requirements are taken from the user through questions. The drawback of this approach is user may not have the same adaptation requirements from the system all the time.


This approach is aided with the machine so that the user can provide enough information to the machine to adapt itself to complete the user task in the possible shortest path. The constraint of the question and answer sessions is the requirement of an individual user for adaptation may not be the same for all the situations. In order to overcome the disadvantage the question and answers sessions are to be conducted in frequent intervals for better adaptation.

The aim of CAA (Computer Aided Approach) is to provide good visibility of adaptation of the system according to the user specifications.

The user is given access to the embedded user model, adaptive mechanism and the dialog. This provides the transparency to the user about the adaptive process and can overcome the confusions of irrelevant adaptations.


Inspecting system's inspecting adaptation interacting with application;

User model mechanisms inspecting and adapting dialog

User model




(retrieved from page2 Thomas Kuhme, 1993 )

In the above figure the system model for the adaptation is shown. The user is given access to the user model and adaptive dialog. Since the user is given access to embedded model the user has a clear idea of the adaptation of the system. The User


model is used to inspect the adaptation mechanisms and the adaptation is done through Adaptive dialog.

Management of Adaptive Computing Techniques using Hardware Components

Now-a-days microprocessor technology has become the base in designing the interfaces for efficient and effective human computer interaction. When the user interacts with the machine, a request is sent and a response is generated which is handled by microprocessor. The pressure on the processors for requests and responses has been increased due to the challenges for accuracy and processing speed of the machine.

Adaptive computing can be implemented for finding the optimal way to accomplish the task i.e., the shortest way of completing the job. The adaptive computing environment has several hardware components which explore the most efficient way to complete the task. Performing the task in shortest path improves the accuracy and processing speed of the machine.

Code regions or hotspots are the code sequences which are often executed by the program. A hardware component system usually includes the following steps to detect and optimize hotspots. Initially, a program code block is interpreted and executed. The execution frequency information of the code block is then gathered by the interpreter and saved in the code block. The information is then examined to find frequently executed code blocks as hotspots, and advanced optimizations are applied on them. (Hu et al., 2005)

“Dynamic optimizers find out the important code regions (phases) in the programs, these code regions are used to find the way for adaptation. Methodologies have been developed for the


adaptation, from the code regions identified by the dynamic optimizers. There are two methods for detecting the change of phases in the code, they are: temporal and positional. In the temporal approach the execution scheme is divided into two intervals and phase change is recognized when two successive intervals behave differently. In the positional approach, phase change is detected through boundary checks.” (Hu et al., 2005)

In the adaptive computing environment usage of the resources in an effective manner for completing tasks required by the user is most important job.

Adaptive User Interfaces Proficiencies

In order to follow ever-changing guidelines and standards, the best way is to use adaptive user interfaces. Simplifying user interfaces to make it convenient for users is a challenging task to the developers. A good user interface can be used in a wide range of applications.

Basically there are two techniques of Adaptive User Interfaces:

A) Adaptive Presentation.

B) Adaptive Navigation.

Adaptive Presentation:

The aim of Adaptive Presentation is presenting information to the user by knowing the user's characteristics. An efficient user may be presented with detailed information where as a naïve user should be provided with exact data for easy understanding. This technique hides the unwanted information i.e. low level data. Consider a school management system it is software in which the user can be a teacher, a student and also the head master. If the user is a teacher with few rights then only a few fields are visible and rest are


hidden from the user. Those hidden fields are displayed to the head master with all rights to access it.

Adaptive Navigation:

Adaptive Navigation provides user with the correct path to reach a goal in short time. It guides the user with the relative position of what and also it directs the user to the desired location based on the user's characteristics. An expert user needs very little information where as a naive user has to be guided step by steps to navigate. Adaptive Navigation provides the user with specific knowledge based on the user type.

Combining Processing Models of User Information for Adapting

The process of finding the nonverbal information (eye fixations, facial expressions, upper-body posture, arm movements, and keystroke force etc...), of the user and using it to adapt the system according to the mental mood of the user. This is nonverbal information of the user which is used by perceptual processing block.

“HCI (Human Computer Interaction) has developed using two competing methodologies direct manipulation and intelligent agents (also known as delegation). These approaches can be contrasted as the computer sitting passively waiting for input from the human versus the computer taking over from the human. Another dimension for HCI is that of affective computing. Affective computing is concerned with the means to recognize “emotional intelligence.” ” ( Duric et al., 2002)


(retrieved from page3 Duric et al., 2002 )

In the above architecture for adaptation, the main blocks are perceptual processing, behavioral processing and embodied cognition. The user and system will interact continuously and the system will adapt according to the user's nonverbal information collected by the perceptual and behavioral processing blocks.

The behavioral processing block concentrates on the data inputs from the keyboard and mouse of the machine. The perceptual processing block concentrates on finding the nonverbal user information such as gestures and expressions of the user. Both the processing blocks collect the user information and forward it to the embodied cognition to find the steps to accomplish the user tasks.

“The embodied cognition module has at its core an embodied cognitive model and a model tracing function. A cognitive model is capable of solving tasks using the same cognitive steps as humans use to solve the tasks.” ( Duric et al., 2002). This information is used to adapt the system

The adaptation of the user/system interface is done according to the architecture and data collected from the behavioral, perceptual and cognition modules. “The types of interface adaptations that one can consider include: 1) addition and deletion of task details; 2) addition and deletion of help/feedback windows; 3) changing the formatting/organization of information; and 4) addition and removal of automation of simple subtasks.” ( Duric et al., 2002)

The type of adaptation is decided by the nonverbal information of the user through the perceptual processing block and the behavioral interaction between the user and the system. The


interface adaptation types are designed for effective interaction between the user and the system which results in adaptive and intelligent human computer interaction.

Adaptive User Interfaces to improve Visualization of map-based data in Mobile devices

Mobile devices present user's with the information for visualizing data required by the user for navigation. Considering a group of user's requirements rather than one individual and also for better performance and to eliminate awkward interfaces adaptive user interfaces were proposed. “Adaptive user interfaces, which adapt to the individual characteristics of the user, are proposed as an alternative approach to improve Mobile Map-based Visualization (MMV) systems” ( Tonder et al., 2008). The basic concept of adaptive user interfaces is the System should adapt to user not the vice versa.

Adaptive user interfaces are incorporated into mobile devices which have performance problems. Although existing techniques used in Mobile devices for visualization are from desktop visualization systems they face many problems which are solved by adaptive visualization techniques. The adaptive user interfaces of mobile devices record the user data and understand the mental model of the user and determine how to adapt to requirements of the user.

Clustering of multimedia data is seen in adaptive user interfaces which helps user from manually organizing the data.

Multimodal Adaption:

Performance of mobile devices is improved by attaching SOAP (Sensing, Operating and Activating Peripheral) box to it and problems of traditional map based devices can be eliminated


by this box. In traditional map based devices screen resolution and scrolling have limited capabilities. The mobile devices with SOAP uses the hand gestures for selecting options it presents the user with High resolution multimedia data with a good overview by zooming option.

Enhanced interfaces in map-based mobile devices:

The information in the map based devices is affected by the zooming option so information borders are added to the devices which capture or hold the icons and information for clear visibility. The below figure depicts how the map based mobile devices with adaptive user interfaces show high resolution data on user end with deep zooming options.

(Retrieved from page 261 Tonder et al., 2008)

The adaptive user interfaces in MMV systems capture videos, photographs and longitude and latitude positions for rendering the information are responsible for clustering the multimedia data to reduce effort of organizing it at user end. The mobile devices which include Adaptive interfaces overcome bandwidth problems by pre-caching the user required data.

Adaptable User Interfaces for visuallyafflicted people

Presenting graphics from the web to a user based on different user requirements is a great challenge in this modern era. Adaptive user interfaces for visually impaired presents the output


suitable to view by self adjusting the system. There are interoperable systems to adjust to user's graphical requirements. “This system brings the opportunity to visually impaired people to gain access to graphics via different modalities by providing an adequate accessibility interface and interaction based on their profiles and needs.”(McAllister et al., 2007)

2D/3D devices and haptic technologies enhance the internet accessibility which adapt according to the user's requirement by giving assistance to the existing technology and have their unique functionality. The graphic content present in the system like images, maps are modified and rendered for easy visibility. Different users have different ways of interpreting things so these interfaces are so flexible to adapt and adjust to various inputs and give the desired output.

Difference between current and proposed adaptable user interface:

(Retrieved from page 1539 McAllister et al., 2007)

The above figure explains the flexibility of proposed adaptable user interface

This adaptable user interface is compatible with most browsers today for displaying information to the user and also extensible which can be altered when necessary to avoid re-building of the system, and is also interoperable many web graphics with different technologies can be accessed using the interoperable user interfaces. There is communication between


interoperable user interfaces with in the system for performing tasks. The system combines the current technology and presents user with more refined user interface.

Future of Adaptive User Interfaces

Human Computer Interaction can be enhanced by the situational information presented to the system. This information includes the details about the user, the environment and the resources of computing. The information sent to the system may change according to the user requirements .The interfaces which dynamically modifies its behavior and adapt to the situations are called Adaptive User Interfaces.

Adaptive Interfaces with Collaborative work activities:

The services supporting adaptive interfaces are not able to meet the user requirements and are very much confined and focus on few situations. In personalized learning single or few users context was considered for which was not able to meet the requirement of other users.

Collaborative work activities come into play which considers a group of users rather than an individual to get the required information. “Collaborative work activities present a challenging personalizationsituation - no longer are we adapting to an individual in a well understood domain, as in personalized eLearning. Rather we are adapting to a group of users involved in a collaborative process.” (Conlan et al., 2003)

Regular users and also naïve users are not just using web for information browsing but also for the most basic functions in daily life, business transactions and jobs. There are efficient, easily adjustable and adaptable user interfaces to handle many operations. Most of the web browsers today which use adaptive user interfaces render only low level issues of web content


and fail to show high level and important issues. Aurora a web browser has its own Approach; work activities are collaborative which is user oriented and focus on group of user requirements rather than one.

Web objects are analyzed based on semantic functions in Aurora “For example, within the context of a search-engine, transcodes a web object as a search-box rather than just an HTML form element. The goal is to make the abstract services provided by these collections accessible to the broadest population of users.”( Sundaresan et al., 2000)

eBay Search

Search for an Item

eBay Items List

2 items found for “book”

1)Big book 10$ 5 bids

2)Animal planet 24 $ 7bids

(Retrieved from page 125 Sundaresan et al.,2000)

The above figures are web objects of the browser Aurora.

(Retrieved from http://milesdowsett.com/index.php/blog/article/adaptive-paths-aurora-interface-concept-unveiled/)

The above interface is Aurora; it is a browser in which objects are invoked by user gestures which is a good adaptive user interface. These interfaces like Aurora are the roots to the


next generation user interfaces which are context -aware systems which are mainly built on user centered design.



Technological advancements in Human Computer Interaction led to the advent of user friendly, extensible, interoperable adaptive user interfaces. These interfaces focus on personalization and adapt according to the user requirements. The interfaces even respond to user gestures and also to speech. They have flexible interfaces for monitoring the user and system. They play a key role in web applications, mobile devices and also many more functionalities.

The interaction with a computer has become more accessible and users expect interaction with system in a natural way similar to interaction with a human. So designers aim at developing intelligent user interfaces which focus is on performance of the system to substitute human process and effort which take necessary feedback from users and adapt accordingly.

(Retrieved from http://piksels.com/introduction-to-adaptive-uis/)

The above figure depicts that future user interfaces will be more intelligent and adaptive. The flow shows the present trends in human computer interaction and improvements in human computer interaction and upcoming advancements the intelligent user interfaces which aim at reducing human for interaction.



1) Shiwen Hu., Madhavi Valluri., Lizy Kurian Jhon. 2005. Effective Adaptive Computing Environment Management via Dynamic Optimization. Code Generation and Optimization, 63 - 73. doi: 10.1109/CGO.2005.17

2) Kumar, M., Gupta, A., and Saha, S. 2006. An approach to adaptive user interfaces using interactive media systems. In Proceedings of the 11th international Conference on intelligent User interfaces (Sydney, Australia, January 29 - February 01, 2006). IUI '06. ACM, New York, NY, 312-314. DOI= http://doi.acm.org/10.1145/1111449.1111521

3) Arroyo, E. and Selker, T. 2003. Self-adaptive multimodal-interruption interfaces. In Proceedings of the 8th international Conference on intelligent User interfaces (Miami, Florida, USA, January 12 - 15, 2003). IUI '03. ACM, New York, NY, 6-11. DOI= http://doi.acm.org/10.1145/604045.604051

4) Thomas Kuhme. 1993. A user-centered approach to adaptive interfaces. International Conference on Intelligent User Interfaces, 243-245.

doi: http://doi.acm.org/10.1145/169891.170012

5) Duric, Z. Gray, W.D. Heishman, R. Fayin Li Rosenfeld, A. Schoelles, M.J. Schunn, C. Wechsler, H. 2002. Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction. Proceedings of the IEEE. 90(7), 1272 - 1289. Retrieved from:


6) Reuss, R., Munoz, J., Miyazaki, T., Bagherzadeh, N., Banerjee, P., Hutchings, B., and Schott, B. 2003. Adaptive computing: what can it do, where can it go?. In Proceedings of the 2003 Asia and South Pacific Design Automation Conference (Kitakyushu, Japan, January 21 - 24, 2003). ASP-DAC '03. ACM, New York, NY, 463-463. DOI= http://doi.acm.org/10.1145/1119772.1119866

7) Wolfgang Stuerzlinger., Olivier Chapuis., Dusty Phillips, Nicolas Roussel. 2006. User interface façades:towards fully adaptable user interfaces. Symposium on User Interface Software and Technology, 309-318. doi: http://doi.acm.org/10.1145/1166253.1166301


8) Chui Chui Tan., Wai Yu.,Graham McAllister. 2007. An adaptive & adaptable approach to enhance web graphics accessibility for visually impaired people. Conference on Human Factors in Computing Systems, 1539 - 1542.

doi: http://doi.acm.org/10.1145/1240624.1240856.

9) Joel Laumens. March 4, 2009. Introduction to adaptive UI's. Retrieved from: http://piksels.com/introduction-to-adaptive-uis/

10) Seth Rogers and Wayne Iba, Cochairs. 2008 Adaptive User Interfaces. Retrieved from: http://www.aaai.org/Press/Reports/Symposia/Spring/ss-00-01.php

11) Owen Conlan., Ruaidhri Power., Steffen Higel., Declan O'sullivan., keara Barrett. 2003.

Next generation context aware adaptive services. ACM International Conference Proceeding Series, 49, 205-212.

12) Anita, W., Huang., Neel, Sundaresan. 2000. Aurora:a conceptual model for Web-content adaptation to support the universal usability of Web-based services. ACM Conference on Universal Usability, 124-131.

doi: http://doi.acm.org/10.1145/355460.355546.

13) Bradley Van Tonder., Janet Wesson. 2008. Using adaptive interfaces to improve mobile map-based visualization. ACM International Conference Proceeding Series, 338, 257-266.

doi: http://doi.acm.org/10.1145/1456659.1456689

14) Krish, Ramachandran. 2009. Adaptive user interfaces for health care applications.

Retrieved from : http://www.ibm.com/developerworks/web/library/wa-uihealth/

15) Tapani, Rantakokko.,Johan Plomp. 2004. An Adaptive Map-Based Interface for Situated Services.

Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=