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User modelling for adapted accessible interaction

User modelling for adapted accessible interaction. Julio Abascal # , Olatz Arbelaitz * , Myriam Arrue # , Javier Muguerza * # EGOKITUZ: Laboratory of HCI for Special Needs * Algorithms, Data mining and Parallelism Research Team University of the Basque Country/Euskal Herriko Unibertsitatea.

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User modelling for adapted accessible interaction

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  1. User modelling for adapted accessible interaction Julio Abascal#, Olatz Arbelaitz*, Myriam Arrue#, Javier Muguerza* # EGOKITUZ: Laboratory of HCI for Special Needs * Algorithms, Data mining and Parallelism Research Team University of the Basque Country/Euskal Herriko Unibertsitatea

  2. Rationale • This paper briefly describes the diverse approaches to user adapted accessible interaction that we have developed in the last years • Purpose: To discuss the possibility of advancing towards a comprehensive approach to shared-user modelling

  3. Index • Introduction of EGOKITUZ • Objectives • Personal accessibility to the web • Accessibility to Ubiquitous Computing environments • Web mining for user modelling • Conclusions

  4. EGOKITUZ: Laboratory of HCI for Special Needs • Created in 1985. • Main goal: the application of computer technology to provide support to people with disabilities and elderly people. • Staff: Variable (currently 10 fulltime researchers). http://go.ehu.es/Egokituz

  5. EGOKITUZ: Laboratory of HCI for Special Needs • Research • HCI & Assistive Technology • Ambient Intelligence & Ubiquitous Computing • Web Accessibility • Teaching • Advanced interaction systems • Networks, OS & HW design • Accessibility & Usability • International activities • IFIP TC13 Human-Computer Interaction (1991-) • IFIP WG 13.3 HCI and Disability (1993-) • IFIP WG 13.1Education in HCI and HCI Curriculum(1999-) • EU • Adviser, reviewer, evaluator, expert roles • EU projects, COST European actions

  6. Index • Introduction of EGOKITUZ • Objectives • Personal accessibility to the web • Accessibility to Ubiquitous Computing environments • Web mining for user modelling • Conclusions

  7. Objectives • Enhancing the accessibility for people with temporary or permanent restrictions. • Adapting the interaction system to • the features, needs and likes of each specific user, and • the characteristic of each place and task. • By compiling information about the users and their environment to create suitable user models • And dynamically creating personalized interfaces. • Future: Sharing or exporting modelsamong remote applications

  8. Index • Introduction of EGOKITUZ • Objectives • Personal accessibility to the web • Accessibility to Ubiquitous Computing environments • Web mining for user modelling • Conclusions

  9. Universal Accessibility to the web • The problem of Web accessibility is mainly treated from the Design for All or Universal Accessibility point of view. • This approach is extremely convenient for ensuring the civil rights to electronic inclusion of people with any type of disability. • Many methodologies and tools have been created to apply these guidelines. • This approach failed to help specific users to find suitable web sites

  10. EvalAccess:Automatic Web Accessibility Evaluator A result of the IRIS European Project: • Built as a web-service to be used from mainstream applications. • Not built-in Guidelines: able to evaluate diverse sets of guidelines. • Tool to allow the creation of machine-readable new guidelines: specific purpose guidelines. • It provides statistical data to create quantitative accessibility metrics.

  11. Personal vs. Universal Accessibility • Evalaccess allowed us to tackle Personal Accessibility: • Starting from a combination of • Quantitative metrics and • The use of specific guidelines or WAI subsets • In order to select the most adequate guidelines users where modelled. • Abilities and restrictions to access the Web

  12. Index • Introduction of EGOKITUZ • Objectives • Personal accessibility to the web • Accessibility to Ubiquitous Computing environments • Web mining for user modelling • Conclusions

  13. EGOKI INREDIS Project • INterfaces for RElations between Environment and people with DISabilities • Consortium: 14 companies, 18 research institutions. • Period: 2007 to 2010. • Investment €23.6 million. • Purpose: to develop basic accessible and interoperable technologies that enable the communication and interaction between people with disabilities and their environment. • Some work-packages: • Interoperability protocols. • Assistive technology and ubiquitous software. • Adaptive user interfaces and device configuration. • Interoperability in mobile devices. • http://www.inredis.es/Default.aspx. • INREDIS project inspired us to create EGOKI

  14. Scenario: Interacting with Ubiquitous Computing Environments • Middleware • 3. The system creates (and downloads to the user device) an instance of the UI adapted to the user/device/context • 1. The user device and the target machine somehowtransparently communicate (through a wireless network) • 2. The ATM service is offered to the user. He/She accepts it (using his/her mobile personal device)

  15. Automatic generation of accessible User Interfaces EGOKI: Automatic generation of adapted UIs for ubiquitous computing • For users with restrictions: • people with disabilities, elderly people. • people performing other activities (driving) or using special devices (mobiles). • Goal: to provide ubiquitous access to “intelligent machines” (ATMs, information kiosks, intelligent home appliances, etc.). • Context: users are provided with their own device adapted to their features and needs.

  16. Automatic generation of accessible User Interfaces • Service designers provide abstract specifications of the UI for each service by means of a User Interface Modelling Language (UIML) • The system maintains user/task/context models (in an ontology) • EGOKI selects the most suitable interaction resources (from those supplied by the service provider) taking into account the user’s capability for each communication modality. • It creates an accessible adapted UI, which is suited to the service.

  17. Automatic generation of accessible User Interfaces Case Study: Underground Ticketing

  18. Validation • In order to to prove the correct functionality and the accessibility of interfaces that the EGOKI generated automatically, it was carried out: • Barrier Walkthrough exercise • User Based Testing: Blind users and Users with cognitive disabilities

  19. Index • Introduction of EGOKITUZ • Objectives • Personal accessibility to the web • Accessibility to Ubiquitous Computing environments • Web mining for user modelling • Conclusions

  20. User Modelling based on Web Usage Mining • Data acquisition and pre-processing • Complex (the most time consuming and computationally expensive step) • Users’ privacy issue. • Diverse possible sources (client machines, proxies, servers...) • It includes: • user and session identification, and • data fusion and cleaning. • Pattern discovery and pattern analysis. • Machine learning techniques are applied in order to find sets of web users with common web-related characteristics and the corresponding patterns. • Paradigms : unsupervised learning (or clustering), association rules, and paradigms used to find frequent patterns such as frequent episodes. • Subsequently: selection of the most meaningful patterns. • manually by experts in the area or • based on the parameters of the machine learning algorithms used

  21. ModelAcces Project • Currently we are working on profiling functional abilities of the users, using data extracted from their web interaction. • Logs from a large website DISCAPNET run by ONCE. • Some variables automatically extracted from the server log data, can have direct connections with t user’ abilities: • number of different URLs visited • average time spent on each URL (taking into account if the page is of authority type or hub type) • maximum and/or average depth of each session • diversity in semantic content of the visited URLs • etc. • We use these types of parameters to make assumptions about the possible limitations of the users (specific disabilities, how lost they are, etc.). • The results will be used to enrich the recommendations generated using other strategies.

  22. Conclusion • Proliferation of adaptive applications (each one handling and maintaining its own model). • But Public Ubiquitous Computing environments do not have a model of each user. • Is it possible to share models among applications? • Development of methods to… • …(partly or completely) share models. • …provide remote access to private models. • …define formats for interoperable model description. • …protect user privacy. • …adopt “Virtual UserModelling” [VUMS White Paper].

  23. EGOKITUZ: Laboratory of HCI for Special Needs Location University of the Basque Country/ Euskal Herriko Unibertsitatea Donostia. Spain

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