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Indoor Location Data for Tracking Human Behaviours: A Scoping Review.

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Summary

Real-time location systems (RTLS) generate spatiotemporal data to reveal human behavior patterns. Analyzing features like dwell time and activity levels from RTLS data enhances understanding across various applications.

Keywords:
computational intelligencedata analyticsdigital phenotypinghealth monitoring technologieshuman behaviourreal-time location systemssensor-based assessments

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Area of Science:

  • Human-Computer Interaction
  • Ubiquitous Computing
  • Behavioral Science

Background:

  • Real-time location systems (RTLS) collect spatiotemporal data crucial for understanding human behavior patterns.
  • Location data from RTLS has diverse applications, including contact tracing, health monitoring, and efficiency analysis.
  • Examining how RTLS data describes behavior is essential for maximizing its utility.

Purpose of the Study:

  • To review behaviors described using indoor location data from RTLS.
  • To identify and categorize features extracted from RTLS data for behavior analysis.

Main Methods:

  • Systematic review of studies utilizing indoor RTLS data.
  • Categorization of identified behaviors into four major application areas: health status monitoring, consumer behaviors, developmental behavior, and workplace safety/efficiency.
  • Classification of RTLS data features into four groups: dwell time, activity level, trajectory, and proximity.

Main Results:

  • Four key application areas for RTLS-described behaviors were identified.
  • RTLS data features were categorized into dwell time, activity level, trajectory, and proximity.
  • Passive sensors with non-uniform data streams and lower complexity features were commonly used.
  • Limited analysis of social behaviors involving multiple individuals and inconsistent examination of clinical validity in health monitoring studies were noted.

Conclusions:

  • Spatiotemporal data from RTLS effectively identifies behavior patterns.
  • Effective use of RTLS data requires sufficient location data richness, well-defined behaviors, and detailed feature analysis.
  • Further research is needed on social behavior analysis and clinical validation of RTLS in health monitoring.