A subject state detection approach to determine rest-activity patterns using load cells

Adriana M. Adami, André G. Adami, Gilmar Schwarz, Zachary T. Beattie, Tamara L. Hayes

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

6 Scopus citations

Abstract

A patient's sleep/wake schedule is an important step underlying clinical evaluation of sleep-related complaints. Aspects related to timing of a person's sleep routine provide important clues regarding diagnosis and treatments. Solutions for sleep complaints may sometimes rely solely on changes in habits and life style, based on what is learned from daily restactivity patterns. This paper describes an approach for determining two states, in-bed and out-of-bed, using load cells under the bed. These states are important because they can help characterize rest-activity patterns at nighttime or detect bed exits in hospitals or nursing homes. The information derived from the load cells is valuable as an objective and continuous measure of daily patterns, and it is particularly valuable in sleep studies in populations who would not be able to remember specific hours to complete sleep diaries. The approach is evaluated on data collected in a laboratory experiment, in a sleep clinic, and also on data collected from residents of an assisted-living facility.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages204-207
Number of pages4
DOIs
StatePublished - 2010
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period8/31/109/4/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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