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Monitoring changes in behaviour from multi-sensor systems.

James D Amor1, Christopher J James1

  • 1Warwick Engineering in Biomedicine , School of Engineering , University of Warwick , Coventry CV4 7AL , UK.

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|November 27, 2015
PubMed
Summary

Automated behavior monitoring using multi-sensor environments can detect health changes. A new method quantifies behavioral shifts, successfully differentiating weekday and weekend activity patterns.

Keywords:
automated behaviour-monitoring systemsbehavioural patternsbehavioural sciences computingdata analysis methodhealth statusmedical signal processingmultisensor systemspatient monitoringsensor fusion

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Area of Science:

  • Health Informatics
  • Sensor Technology
  • Behavioral Science

Background:

  • Behavioral patterns are key health indicators, but current monitoring relies on outdated paper methods.
  • Technological advancements enable automated behavior monitoring via multi-sensor environments.
  • Large data volumes from sensors present challenges for extracting meaningful insights.

Purpose of the Study:

  • To introduce a novel method for detecting behavioral patterns.
  • To develop a metric for quantifying behavioral change in multi-sensor data.
  • To validate the method's ability to distinguish between different activity periods.

Main Methods:

  • A novel data analysis method was developed for multi-sensor environments.
  • The method focuses on detecting behavioral patterns and quantifying change.
  • Experimental validation was performed with two participants.

Main Results:

  • The proposed method successfully detected differences between weekdays and weekend days.
  • Behavioral change metrics showed significant differences (95% confidence level) for weekdays vs. weekend days.
  • The findings were consistent across different sensor configurations and environments.

Conclusions:

  • The novel method effectively quantifies behavioral change in automated monitoring systems.
  • This approach can reliably differentiate between distinct behavioral patterns, such as weekday and weekend routines.
  • Automated behavior monitoring offers a promising avenue for health status assessment.