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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.
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Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review.

Mariana Jacob Rodrigues1,2, Octavian Postolache1,2, Francisco Cercas1,2

  • 1Iscte-Instituto Universitário de Lisboa, Av. das Forças Armadas, 1649-026 Lisboa, Portugal.

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Summary
This summary is machine-generated.

Internet of Things (IoT) in healthcare enables remote monitoring of health and environment for personalized care. This technology addresses challenges of an aging population and improves healthcare quality through smart environments.

Keywords:
activity recognitionhealthcareindoor air qualityinternet of thingsphysiological signs monitoringsmart environments

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Area of Science:

  • Healthcare technology
  • Digital health
  • Gerontology

Background:

  • Increasing demand for high-quality healthcare services and challenges posed by population aging.
  • The Internet of Things (IoT) offers a solution for preventing and diagnosing health impairments.
  • IoT applications in healthcare include remote monitoring of environmental factors and human physiological parameters.

Purpose of the Study:

  • To address requirements for developing assisted and smart healthcare environments.
  • To explore the deployment of physiological monitoring systems, activity recognition, and indoor air quality monitoring.
  • To review machine learning methods for activity recognition and discuss exergames for elderly training.

Main Methods:

  • Review of existing literature on IoT in healthcare.
  • Identification of requirements for smart healthcare environments.
  • Analysis of machine learning techniques for activity recognition.
  • Discussion of exergames and immersive environments for elderly training.

Main Results:

  • IoT enables remote monitoring of physiological signs, daily activities, and environmental parameters.
  • Machine learning is crucial for activity recognition and body motion analysis.
  • Smart environments can provide personalized healthcare and support elderly training.

Conclusions:

  • IoT implementation is vital for optimizing healthcare, especially for the aging population.
  • Development of integrated systems for monitoring and personalized interventions is essential.
  • Exergames and immersive technologies hold promise for enhancing elderly physical and cognitive health.