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Modeling Patterns of Activities using Activity Curves.

Prafulla N Dawadi1, Diane J Cook1, Maureen Schmitter-Edgecombe2

  • 1School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA.

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Summary
This summary is machine-generated.

Pervasive computing uses smart home sensors to create activity curves, representing daily routines. Detecting changes in these patterns can help assess cognitive and physical health changes in older adults.

Keywords:
Activity CurveFunctional AssessmentPermuationSmart Environments

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Area of Science:

  • Pervasive computing
  • Machine learning
  • Health informatics

Background:

  • Pervasive computing enables unobtrusive behavior monitoring and analysis of activity-based patterns.
  • Analyzing behavioral routines can provide insights into cognitive and physical health.

Purpose of the Study:

  • Introduce the concept of an 'activity curve' to abstract daily routines.
  • Develop methods to detect changes in behavioral routines using activity curves.
  • Analyze the correlation between detected behavioral changes and potential health alterations.

Main Methods:

  • Utilized smart home sensor data from 18 homes with older adult residents.
  • Developed and applied activity curve generation and comparison techniques.
  • Employed machine learning and big data analytics for change detection.

Main Results:

  • Demonstrated the feasibility of automatically detecting changes in behavioral routines.
  • Found correlations between detected behavioral shifts and changes in cognitive or physical health.
  • Validated the use of pervasive analytics for functional health assessment.

Conclusions:

  • Activity curve-based change detection in smart homes is a viable method for monitoring health.
  • Pervasive analytics can automatically identify health-related behavioral changes.
  • This approach supports early detection and functional health assessment in older adults.