Combinations of Modalities for the Words Learning Memory Test Implemented on Tablets for Seniors

  • Erika Hernández-Rubio
  • Amilcar Meneses-ViverosEmail author
  • Erick Mancera-Serralde
  • Javier Flores-Ortiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9754)


Mnesic problems in older adults is a global health problem. Some proposals have been made to support health care in older adults using mobile technologies. In particular,there are analysis and design of mobile applications focusing on the elderly to apply memory tests Luria. For example, in word learning test, multiple words or numerical figures unraleted are shown to the patient. The number of item exceed the numer that the patient can remember. Usually the serie consist of ten or twelve words or numerical digits. After this task, the patient is asked to repeat the series in any order. In one hand, the physical deterioration of the elderly makes it difficult the usability of the user interface of mobile applications. These deteriorations can be auditory, visual and motor. They are particular to each elderly. For this reason, a traditional user interface loses effectiveness when it has interaction with older people. In another hand, some studies suggest that Tablets are the best mobile devices for older adults, because the size of their screen and usability of their user interface. However, tablets have different modes of interaction and it is not yet clear how the elderly respond to them. One solution to this problem is to provide different modalities for interaction. This modalities they must be presents in applications for tablets. In this work, we present the implementation of the test world learning Luria memory test. And we implemented several combinations of modalities for the test. Finally we present the result of interaction with the older adults.


Mobile Device Mobile Application Word Learn Multimodal Interface Prototype Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Erika Hernández-Rubio
    • 1
  • Amilcar Meneses-Viveros
    • 2
    Email author
  • Erick Mancera-Serralde
    • 3
  • Javier Flores-Ortiz
    • 3
  1. 1.Instituto Politécnico NacionalSEPI-ESCOMMéxico D.F.Mexico
  2. 2.Departamento de ComputaciónCINVESTAV-IPNMéxico D.F.Mexico
  3. 3.School of ComputingInstituto Politeécnico NacionalMéxico D.F.Mexico

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