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Recommending Video Content for Use in Group-Based Reminiscence Therapy

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Health Monitoring and Personalized Feedback using Multimedia Data

Abstract

REMPAD is a semi-automated cloud-based system used to facilitate digital reminiscence therapy for patients with mild-to-moderate dementia, enacted in a group setting. REMPAD uses profiles for participants and groups to proactively recommend interactive video content from the Internet to match these profiles. In this chapter, we focus on the design of the system and then the system architecture, the system build, data curation, and usage scenarios. We also report a series of steps carried out as part of our user-centered design approach to system development, and a series of analyses on interaction logs which indicate various levels of effectiveness for different configurations of the recommendation algorithm we use. The results indicate high user satisfaction when using the system, and strong tendency towards repeated use in future.

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Notes

  1. 1.

    http://www.crowdflower.com.

  2. 2.

    http://lucene.apache.org/solr/.

  3. 3.

    For this reason we also provide favourites and history functions which are sometimes used.

  4. 4.

    As this is not a controlled study, our ethical approval does not extend to using a control as one of our experimental conditions. This has precluded us from exposing people with dementia to potentially weak experimental conditions such as randomly selected content which might not suit their tastes.

  5. 5.

    In practice this was difficult to maintain as users often created impromptu sessions for training and testing purposes which were later removed from the trial data.

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Acknowledgements

This work is supported by Science Foundation Ireland under grants 07/CE/I1147 and SFI/12/RC/2289 and by Enterprise Ireland under grant CF/2011/1318.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, Dublin City University, Glasnevin, Dublin 9, Ireland

    Adam Bermingham, Niamh Caprani, Cathal Gurrin, Alan F. Smeaton & Yang Yang

  2. Age-Related Health Care, Adelaide and Meath Hospital Tallaght, Dublin, Ireland

    Ronán Collins

  3. School of Nursing and Human Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland

    Kate Irving

  4. Department of Speech and Language Therapy, Adelaide and Meath Hospital Tallaght, Dublin, Ireland

    Julia O’Rourke

Authors
  1. Adam Bermingham
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  2. Niamh Caprani
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  4. Cathal Gurrin
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  5. Kate Irving
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  6. Julia O’Rourke
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  7. Alan F. Smeaton
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Corresponding author

Correspondence to Alan F. Smeaton .

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Editors and Affiliations

  1. Centre for Research and Technology, Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Alexia Briassouli

  2. Laboratoire Bordelais de Recherche en Informatique/University Bordeaux, Talence, France

    Jenny Benois-Pineau

  3. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Alexander Hauptmann

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Bermingham, A. et al. (2015). Recommending Video Content for Use in Group-Based Reminiscence Therapy. In: Briassouli, A., Benois-Pineau, J., Hauptmann, A. (eds) Health Monitoring and Personalized Feedback using Multimedia Data. Springer, Cham. https://doi.org/10.1007/978-3-319-17963-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-17963-6_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17962-9

  • Online ISBN: 978-3-319-17963-6

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