TV Applications for the Elderly: Assessing the Acceptance of Adaptation and Multimodality

Current TV applications present uncountable challenges to an elderly user and these are prone to increase, working as a vehicle of exclusion. The GUIDE project aims at improving the elderly experience with present TV applications by deploying interfaces adapted to their abilities and preferences. We do so by building the interface based on a user model and providing new ways to interact with the TV (multimodality), and tailoring the UI to the users’ abilities, needs and preferences. In this paper, we assess concepts of the GUIDE framework with particular focus to the User Initialization Application (UIA), an interactive application able to build the aforementioned user model. We report an evaluation with 40 older users from two countries (UK and Spain). Results show that the UIA is able to create adequate profiles and that the users are able to positively observe the adaptations. Further, novel ways of interacting with the TV were also successfully evaluated as the users tended to experiment and rate positively most alternatives, particularly Speech and Tablet interaction. Keywords-accessible applications, elderly, multimodal,

[1]  Sara J. Czaja,et al.  The impact of aging on access to technology , 2005, ASAC.

[2]  Patrick Langdon,et al.  Developing Multimodal Adaptation Algorithm for Mobility Impaired Users by Evaluating Their Hand Strength , 2012, Int. J. Hum. Comput. Interact..

[3]  Sharon L. Oviatt,et al.  Mutual disambiguation of recognition errors in a multimodel architecture , 1999, CHI '99.

[4]  Patrick Langdon,et al.  Developing accessible TV applications , 2011, ASSETS '11.

[5]  Peter Robinson,et al.  Designing Inclusive Interfaces Through User Modeling and Simulation , 2012, Int. J. Hum. Comput. Interact..

[6]  Peter Gregor,et al.  Computer use has no demonstrated impact on the well-being of older adults , 2006, Int. J. Hum. Comput. Stud..

[7]  Geraldine Fitzpatrick,et al.  Age matters: bridging the generation gap through technology-mediated interaction , 2009, CHI Extended Abstracts.

[8]  Trevor Darrell,et al.  MULTIMODAL INTERFACES THAT Flex, Adapt, and Persist , 2004 .

[9]  Sharon Oviatt,et al.  Multimodal interactive maps: designing for human performance , 1997 .

[10]  Constantine Stephanidis,et al.  Adaptable and Adaptive User Interfaces for Disabled Users in the AVANTI Project , 1998, IS&N.

[11]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[12]  Eric Horvitz,et al.  The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users , 1998, UAI.

[13]  Martina Ziefle,et al.  Human-Computer Interaction and Usability Engineering for Elderly (HCI4AGING): Introduction to the Special Thematic Session , 2010, ICCHP.

[14]  Vincent G. Duffy,et al.  Handbook of Digital Human Modeling: Research for Applied Ergonomics and Human Factors Engineering , 2008 .

[15]  Vicki L. Hanson Age and web access: the next generation , 2009, W4A.

[16]  Ian Oakley,et al.  Solving multi-target haptic problems in menu interaction , 2001, CHI Extended Abstracts.

[17]  Antonella De Angeli,et al.  Integration and synchronization of input modes during multimodal human-computer interaction , 1997, CHI.

[18]  Benoit M. Macq,et al.  Towards Standardized Pen-Based Annotation of Breast Cancer Findings , 2009, HCI.

[19]  J. Miner,et al.  COLOUR BLINDNESS TESTS , 1950 .

[20]  Daniel B Herren,et al.  Prediction of grip and key pinch strength in 978 healthy subjects , 2010, BMC musculoskeletal disorders.

[21]  Krzysztof Z. Gajos,et al.  Automatically generating user interfaces adapted to users' motor and vision capabilities , 2007, UIST.

[22]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .