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Using the eServices platform for detecting behavior patterns deviation in the elderly assisted living: a case study.

Isabel Marcelino1, David Lopes2, Michael Reis2

  • 1Higher Technical School of Computer Engineering, University of Vigo, Polytechnic Building, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain ; INOV INESC Innovation, Institute of New Technologies of Leiria, 2411-901 Leiria, Portugal ; School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal.

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|April 16, 2015
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Summary
This summary is machine-generated.

This study introduces an alert system to detect behavioral changes in the elderly, preventing potential risks. The system uses data from sensors and the eServices platform to ensure timely assistance and enhance safety.

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Area of Science:

  • Gerontology
  • Computer Science
  • Artificial Intelligence

Background:

  • The global elderly population is growing, leading to increased social and geographical isolation.
  • Elderly individuals often require timely assistance, which is frequently delayed.
  • Existing support systems may not adequately address the unique needs of isolated seniors.

Purpose of the Study:

  • To develop and validate a system for detecting behavioral deviations in the elderly that indicate potential risks.
  • To enhance the safety and well-being of elderly individuals through proactive risk identification.
  • To demonstrate the feasibility of using integrated data sources for elderly care.

Main Methods:

  • Utilized the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology.
  • Integrated data from body area networks (BAN), environmental sensors, and the eServices platform.
  • Employed pattern recognition algorithms to identify deviations from normal behavior.

Main Results:

  • The system demonstrated good performance in preventing alert situations, even with synthetic data.
  • Aggregated variables improved alert system reliability and minimized false positives.
  • The pattern recognition effectively triggered appropriate responses.

Conclusions:

  • The developed system shows promise in proactively identifying and mitigating risks for the elderly.
  • The eServices platform, combined with sensor data, offers a scalable solution for elderly support.
  • Further development with real-world data is expected to enhance predictive capabilities and user safety.