Adapting User Interfaces Based on User Preferences and Habits

In the last few years, an ecosystem of devices and heterogeneous services has emerged with a huge variety of capacities and characteristics. These new devices, along with applications and services, must be used to enhance the quality of life, making the users daily activities easier, as well as increasing their personal autonomy. In this sense, there is a clear need for creating interfaces that adapt themselves taking into account characteristics of the user, context, application and device. One of the aspects to consider when adapting interfaces is the set of preferences of the user. When using different applications or devices, each user has different preferences, mainly related to their limitations. For that, we have developed a system that discovers users preferences related to different devices and applications. The system discovers set-up parameters, as well as normal performance parameters.

[1]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.

[2]  Geoffrey C. Fox,et al.  Adapting Content for Mobile Devices in Heterogeneous Collaboration Environments , 2003, International Conference on Wireless Networks.

[3]  Juan Carlos Augusto,et al.  Designing Smart Homes, The Role of Artificial Intelligence , 2006, Designing Smart Homes.

[4]  Diane J. Cook,et al.  Improving home automation by discovering regularly occurring device usage patterns , 2003, Third IEEE International Conference on Data Mining.

[5]  Moritz Tenorth,et al.  Towards Automated Models of Activities of Daily Life , 2009 .

[6]  E. Campo,et al.  Smart house automation system for the elderly and the disabled , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[7]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[8]  Diane J. Cook,et al.  How smart are our environments? An updated look at the state of the art , 2007, Pervasive Mob. Comput..

[9]  Christophe Le Gal,et al.  Smart Office: Design of an Intelligent Environment , 2001, IEEE Intell. Syst..

[10]  H. Kaye,et al.  Computer and Internet Use among People with Disabilities. Disability Statistics Report 13. , 2000 .

[11]  Juan Carlos Augusto,et al.  Learning patterns in ambient intelligence environments: a survey , 2010, Artificial Intelligence Review.

[12]  Ivan Marsic,et al.  Mobile adaptive applications for ubiquitous collaboration in heterogeneous environments , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[13]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[14]  Fabien L. Gandon,et al.  Ambient Intelligence: The MyCampus Experience , 2005 .

[15]  A. Jameson Adaptive interfaces and agents , 2002 .

[16]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[17]  James F. Allen Towards a General Theory of Action and Time , 1984, Artif. Intell..

[18]  Diane J. Cook,et al.  Learning frequent behaviours of the users in Intelligent Environments , 2010, J. Ambient Intell. Smart Environ..

[19]  N.A. Nijdam,et al.  A context-aware adaptive rendering system for user-centric pervasive computing environments , 2010, Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference.

[20]  Ben Shneiderman,et al.  Split menus: effectively using selection frequency to organize menus , 1994, TCHI.

[21]  Nathalie Cislo Undernutrition Prevention for Disabled and Elderly People in Smart Home with Bayesian Networks and RFID Sensors , 2010, ICOST.

[22]  DAVID G. KENDALL,et al.  Introduction to Mathematical Statistics , 1947, Nature.

[23]  Asim Smailagic Quality of Life Technology: Intelligent Systems for Better Living , 2010 .

[24]  Hani Hagras,et al.  Creating an ambient-intelligence environment using embedded agents , 2004, IEEE Intelligent Systems.

[25]  Diane J. Cook,et al.  Using Temporal Relations in Smart Environment Data for Activity Prediction , 2007 .