Testing classifiers for embedded health assessment

  • Authors:

  • Marjorie Skubic;Rainer Dane Guevara;Marilyn Rantz

  • Affiliations:

  • Electrical and Computer Engineering, University of Missouri, Columbia, MO;Electrical and Computer Engineering, University of Missouri, Columbia, MO;Sinclair School of Nursing, University of Missouri, Columbia, MO

  • Venue:
  • ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
  • Year:
  • 2012

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Abstract

We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture activity patterns. Changes in the activity patterns are detected as potential signs of changing health. A simple alert algorithm has been implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth in developing classifiers. Here, we present the methodology and results for two classification approaches using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The results show similar performance for the two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training.