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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

717
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Related Experiment Video

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from

Amir Ali1, Teodoro Montanaro1, Ilaria Sergi1

  • 1Department of Engineering for Innovation, Università del Salento, 73100 Lecce, Italy.

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|April 28, 2025
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Summary

This study introduces an edge-based Internet of Things (IoT) system for remote elderly monitoring. It uses non-wearable sensors and machine learning for real-time anomaly detection, improving healthcare responsiveness.

Keywords:
IoTanomaly detectionedge computingelderlyhealthcarenon-wearablereal-time health monitoring

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Area of Science:

  • Gerontology
  • Computer Science
  • Biomedical Engineering

Background:

  • Aging global population necessitates advanced remote health monitoring solutions.
  • Current healthcare limitations include lack of continuous behavioral data for elderly patients.
  • Timely medical interventions are hindered by the absence of historical behavioral insights and anomaly detection.

Purpose of the Study:

  • To propose an edge-based Internet of Things (IoT) framework for real-time remote monitoring and anomaly detection in elderly care.
  • To address privacy concerns and ensure immediate data availability through local data processing.
  • To assist doctors and caregivers in assessing elderly patient health and detecting deviations from normal behavior.

Main Methods:

  • Development of an edge-based IoT framework utilizing non-wearable sensors.
  • Implementation of machine learning models, specifically Isolation Forest and Long Short-Term Memory (LSTM) networks, for anomaly detection.
  • Creation of a dashboard for real-time alerts and longitudinal trend visualization for caregivers and healthcare professionals.

Main Results:

  • Demonstration of the system's feasibility, scalability, efficiency, and reliability in elderly care settings.
  • Successful identification of unusual behavioral patterns indicative of potential health risks.
  • Validation of the chosen machine learning models for effective anomaly detection.

Conclusions:

  • The proposed edge IoT framework enhances healthcare responsiveness through instant patient behavior insights.
  • The system facilitates proactive interventions, more accurate diagnoses, and improved medical care for the elderly.
  • This research provides a foundation for integrating edge computing, AI, and IoT in elderly care.