Automated prompting in a smart home environment

Barnan Das, Chao Chen, Nairanjana Dasgupta, Diane J. Cook, Adriyana M. Seelye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

With more older adults and people with cognitive disorders preferring to stay independently at home, prompting systems that assist with Activities of Daily Living (ADLs) are in demand. In this paper, with the introduction of "The PUCK", we take the very first approach to automate a prompting system without any predefined rule set or user feedback. We statistically analyze realistic prompting data and devise a classifier from statistical outlier detection methods. Further, we devise a sampling technique to help with skewed and scanty data sets. We empirically find a class distribution that would be suitable for our work and validate our claims with the help of three classical machine learning algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages1045-1052
Number of pages8
DOIs
StatePublished - Dec 1 2010
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Country/TerritoryAustralia
CitySydney, NSW
Period12/14/1012/17/10

Keywords

  • Automated prompting
  • Machine learning
  • Prompting systems
  • Smart environments

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