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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
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Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
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PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies.

Jitesh Joshi1, Katherine Wang1, Youngjun Cho1

  • 1Department of Computer Science, University College London, London NW1 2AE, UK.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

PhysioKit is an open-source, low-cost toolkit for physiological computing. It offers a modular sensing layer and software for data acquisition, visualization, and ML-based quality assessment, validated against research-grade sensors.

Keywords:
biofeedbackdata acquisition toolkitmulti-user HCI studiesphysiological computingsignal quality assessment

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Area of Science:

  • Physiological computing
  • Human-computer interaction
  • Biomedical engineering

Background:

  • Physiological sensors offer new research avenues but commercial options are expensive and low-cost sensors require significant setup.
  • Access to raw physiological waveform data is crucial for in-depth analysis and user experience evaluation.
  • Existing solutions often present a trade-off between cost, data accessibility, and ease of implementation.

Purpose of the Study:

  • Introduce PhysioKit, an open-source, low-cost physiological computing toolkit.
  • Provide a comprehensive pipeline for physiological data acquisition, processing, and analysis.
  • Validate the performance and usability of PhysioKit in a research context.

Main Methods:

  • Developed a modular sensing and data acquisition layer for flexible configuration.
  • Created a software application layer supporting real-time visualization and machine learning (ML)-enabled signal quality assessment.
  • Integrated basic visual biofeedback and synchronized multi-user acquisition capabilities.
  • Conducted a validation study comparing PhysioKit with research-grade sensors for heart rate and heart rate variability.
  • Performed a usability survey with 10 project teams (44 members) using PhysioKit for 4-6 weeks.

Main Results:

  • PhysioKit demonstrated strong agreement with research-grade sensors in measuring heart rate and heart rate variability.
  • Validation study with 16 participants confirmed the accuracy of the toolkit.
  • Usability survey provided positive insights into PhysioKit's use cases and research benefits.
  • The toolkit supports synchronized acquisition for both co-located and remote multi-user settings.

Conclusions:

  • PhysioKit offers a viable, low-cost, and accessible solution for physiological computing research.
  • The toolkit's modular design and ML-enabled features enhance data acquisition and analysis.
  • PhysioKit has the potential to significantly impact and broaden participation in physiological computing research.