Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Health Monitoring

Background:

• Vital signs (blood pressure, temperature, heart rate, respiration rate) are crucial indicators of overall health.
• Continuous and non-invasive monitoring of vital signs is essential for effective healthcare, especially in home-care settings.
• Existing vital signs monitoring equipment can be cumbersome and expensive for long-term use.

Purpose of the Study:

• To develop and validate a long-term vital signs sensing gown for continuous, non-invasive health monitoring.
• To integrate a miniaturized monitoring device with an intelligent computation platform for vital signs estimation.
• To assess the performance of the sensing gown in clinical settings compared to conventional methods.

Main Methods:

• A wearable sensing gown equipped with a miniaturized device to acquire bioelectrical potentials and skin surface temperature.
• Development of three algorithms for R-wave detection, ECG-derived respiration, and core body temperature estimation.
• Clinical validation involving 31 participants (10 healthy, 21 patients) to assess accuracy.

Main Results:

• The sensing gown accurately derived vital signs including electrocardiogram (ECG) signals, heart rate (HR), respiration rate (RR), and core body temperature.
• Experimental results demonstrated low relative errors for HR (<1.41%) and RR (<5.52%), and minimal bias for core body temperature (<0.04 °C).
• The proposed sensing gown showed superior usability and cost-effectiveness compared to traditional monitoring equipment.

Conclusions:

• The vital signs sensing gown offers a novel, integrated solution for continuous and non-invasive patient monitoring.
• The system's accuracy and performance were validated in clinical practice, proving its potential for home and clinical use.
• This technology presents a promising advancement for remote patient care and pandemic-related health surveillance.