An IR Sensor Based Smart System to Approximate Core Body Temperature.

Area of Science:

• Biomedical Engineering
• Thermal Physiology
• Sensor Technology

Background:

• Accurate core body temperature (BCT) monitoring is crucial for diagnosing and managing various medical conditions.
• Non-invasive methods for BCT estimation are highly desirable for patient comfort and continuous monitoring.
• Existing non-invasive techniques often face challenges with accuracy and energy efficiency.

Purpose of the Study:

• To develop and validate an energy-efficient, non-invasive system for approximating human core body temperature using an infrared (IR) thermographic sensor.
• To evaluate the system's performance under different physiological conditions (rest and post-exercise).
• To assess the potential for integrating the system with IoT cloud services for remote monitoring and prediction.

Main Methods:

• Utilized an industry-grade IR thermographic sensor and an AT Mega 328 microcontroller for data acquisition.
• Employed convection and body resistance approximation methods to estimate core body temperature from skin and ambient temperature readings.
• Implemented Makerplot and JAVA-JAR for real-time data plotting, visualization, and storage.
• Tested the system on human subjects at rest and after 10 minutes of walking.

Main Results:

• The developed system demonstrated high energy efficiency, consuming only 165 mW from a 5 VDC source.
• The non-invasive IR sensor system achieved an average accuracy within 0.7°F compared to a Terumo CoreTemp CM-210 sensor.
• Raw temperature data was successfully plotted, visualized, and stored instantaneously.

Conclusions:

• The proposed IR sensor system offers a viable, energy-efficient, and non-invasive approach for approximating human core body temperature.
• The system's accuracy and low power consumption make it suitable for potential integration into wearable devices.
• Future modifications could enable remote monitoring and predictive health insights via IoT cloud connectivity.