PUCK: An automated prompting system for smart environments: Toward achieving automated prompting-challenges involved

Barnan Das, Diane J. Cook, Maureen Schmitter-Edgecombe, Adriana M. Seelye

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart homebased prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be carried out for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently in their homes. In this paper, with the introduction of the "PUCK" prompting system, we take an approach to automate prompting-based interventions without any predefined rule sets or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that are collected with volunteer participants in our smart home test bed. The data mining approaches taken to solve this problem come with the challenge of an imbalanced class distribution that occurs nturally in the data. We propose a variant of an existing sampling technique, SMOTE, to deal with the class imbalance problem. To validate the approach, a comparative analysis with cost-sensitive learning is performed.

Original languageEnglish (US)
Pages (from-to)859-873
Number of pages15
JournalPersonal and Ubiquitous Computing
Volume16
Issue number7
DOIs
StatePublished - Oct 2012

Keywords

  • Automated prompting
  • Imbalanced class distribution
  • Machine learning
  • Prompting systems
  • Smart environments

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Management Science and Operations Research

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