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Habit Representation Based on Activity Recognition.

Jaeryoung Lee1, Nicholas Melo1

  • 1Department of Robotic Science and Technology, College of Engineering, Chubu University, Kasugai, Aichi 487-8501, Japan.

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|April 3, 2020
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Summary
This summary is machine-generated.

This study presents a new method for assessing daily living habits using smart home activity data. The approach identifies patterns in elderly and young populations, aiding smart home applications in care facilities.

Keywords:
activity recognition systemdistributed sensorselderly supporthabit assessment

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Area of Science:

  • Gerontology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Growing elderly population necessitates advanced care solutions.
  • Smart environments offer potential for remote habit assessment in care facilities.

Purpose of the Study:

  • To develop a novel method for habit assessment using human activity data from smart environments.
  • To represent daily living activities and identify habitual patterns.

Main Methods:

  • Utilized a recognition system with object and ambient sensors to track activities of daily living.
  • Applied Fourier series representation to express activities, with phases and amplitudes denoting sequence and duration.
  • Clustered behavioral spectrum signals to identify habits and calculated variability to determine habit types.

Main Results:

  • Successfully represented activity sequences as behavioral spectra.
  • Identified distinct potential habits for both young and elderly populations through signal clustering and variance analysis.
  • Demonstrated the method's potential in analyzing activity data for habit assessment.

Conclusions:

  • The proposed habit assessment method shows promise for smart home applications in elder care.
  • Findings can enhance the development of assistive technologies for monitoring and supporting daily living.
  • The behavioral spectrum approach offers a novel way to analyze complex human activity patterns.