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Related Concept Videos

Assessing Body Temperature - Temporal Artery01:19

Assessing Body Temperature - Temporal Artery

507
Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
Step 1: Perform hand hygiene and don a fresh pair of gloves to prevent cross-infection and ensure patient safety.
Step 2: Explain the procedure to the patient to establish trust. Clear communication establishes trust with the patient, ensures they understand what to expect, promotes cooperation, and enhances comfort during the procedure.  
Step 3: Assess the patient's...
507
Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

562
Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...
562
Factors Affecting Body Temperature01:28

Factors Affecting Body Temperature

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As a nurse, it is vital to understand the factors affecting body temperature to monitor variations and effectively evaluate deviations from regular.
Factors may  include:
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Assessing Body Temperature - Rectal01:27

Assessing Body Temperature - Rectal

2.7K
Rectal temperature measurement is considered the most precise method for assessing core body temperature and typically registers higher than oral temperature. For adults, the rectal thermometer should be inserted 1 to 1.5 inches into the rectum to obtain the most accurate reading.
Follow these steps for rectal temperature assessment:
Step 1: Perform hand hygiene and don clean gloves to prevent cross-infection.
Step 2: Position the patient in a side-lying position to better visualize the rectal...
2.7K
Decreased Body Temperature01:29

Decreased Body Temperature

583
A decreased body temperature can occur in patients with hypothermia and frostbite. Heat loss with extended cold exposure overpowers the body's ability to create heat, resulting in hypothermia. Core temperature readings help classify hypothermia. Mild hypothermia is temperatures between 32 °C (89.6 °F) and 35°C (95 °F) and is caused by impaired thermoregulation. Moderate hypothermia is temperatures between 28 C (82.4 °F) and 32 °C (89.6 °F) caused by...
583
Body Temperature01:25

Body Temperature

848
The body's temperature, measured in degrees, is determined by the balance between heat production and dissipation to the surrounding environment. For instance, if exercising vigorously, the body will produce more heat, causing sweat and dissipating that heat. Despite extreme environmental conditions and physical exertion, the human temperature-control system maintains a constant core body temperature (the temperature of deep tissues, which are the tissues located beneath the skin and other...
848

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Related Experiment Video

Updated: May 15, 2025

Mouse Body Temperature Measurement Using Infrared Thermometer During Passive Systemic Anaphylaxis and Food Allergy Evaluation
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Prediction of Post-Bath Body Temperature Using Fuzzy Inference Systems with Hydrotherapy Data.

Feng Han1, Minghui Tang2,3, Ziheng Zhang1

  • 1Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-Ku, Sapporo 060-8638, Hokkaido, Japan.

Healthcare (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

Accurately predicting body temperature after hydrotherapy is crucial for safety. An evolutionary fuzzy inference system (EVOFIS) shows promise for predicting deep body temperature, enhancing patient care.

Keywords:
artificial intelligencebody temperature predictionfuzzy inference systemshydrotherapymachine learningnon-invasive monitoringphysiological prediction

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Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere
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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Computational Intelligence

Background:

  • Hydrotherapy leverages water's physical properties for therapeutic benefits, influencing physiological responses.
  • Body temperature modulation is key in hydrotherapy, impacting circulation, muscle relaxation, and metabolism.
  • Improper temperature control in hydrotherapy presents risks, especially for vulnerable populations.

Purpose of the Study:

  • To develop and compare computational models for predicting post-hydrotherapy body temperature.
  • To assess the efficacy of fuzzy inference systems (FIS) against machine learning models for temperature prediction.
  • To enhance the safety and personalization of hydrotherapy through accurate temperature forecasting.

Main Methods:

  • Utilized adaptive neuro-fuzzy inference systems, evolutionary fuzzy inference system (EVOFIS), and enhanced Takagi-Sugeno fuzzy systems.
  • Compared FIS models with random forest and support vector machine models.
  • Employed hydrotherapy-related datasets for model training and validation.

Main Results:

  • The evolutionary fuzzy inference system (EVOFIS) demonstrated superior performance in predicting post-bath body temperature.
  • EVOFIS particularly excelled in forecasting deep body temperature, a critical indicator of physiological regulation.
  • FIS-based models showed potential for non-invasive temperature prediction.

Conclusions:

  • Accurate deep-temperature prediction allows proactive management of hyperthermia risk during hydrotherapy.
  • EVOFIS and other FIS models offer a pathway to safer hydrotherapy practices for at-risk individuals.
  • These findings support the integration of advanced computational models for personalized and safer hydrotherapy applications.