Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt

Area of Science:

• Biomedical Engineering
• Signal Processing
• Respiratory Monitoring

Background:

• Respiratory signal acquisition typically uses wearable sensors.
• Contactless monitoring offers a non-invasive alternative.
• Comparing data from diverse sensors requires robust processing methods.

Purpose of the Study:

• To develop and validate a novel method for measuring, processing, and comparing respiratory signals.
• To assess the compatibility and reliability of data from a wearable sensorized belt and a microwave radar-based sensor.
• To identify sources of variability in respiratory signal measurements.

Main Methods:

• Acquisition of respiratory signals using a wearable sensorized belt and a microwave radar-based sensor.
• Development of a data processing algorithm including octave error elimination and resampling to a common sample rate.
• Statistical analysis using Levene's test, Kruskal-Wallis test, and Dunn's post hoc test to compare signal outcomes.

Main Results:

• The proposed assessment method for respiratory signals demonstrates statistical stability.
• Variability in breathing rate measurements primarily stems from individual breathing patterns.
• The working principles of the wearable belt and microwave radar sensors do not significantly contribute to measurement variability.

Conclusions:

• The novel method effectively processes and compares respiratory signals from different sensor types.
• Individual breathing patterns are the main source of signal variability, not the sensor technology.
• Future work will focus on further refining the analysis and exploring broader applications.