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Related Experiment Video

Updated: Aug 13, 2025

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

Few-Shot User-Adaptable Radar-Based Breath Signal Sensing.

Gianfranco Mauro^1,2, Maria De Carlos Diez¹, Julius Ott^1,3

¹Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a radar system for non-contact respiratory signal prediction in offices. The adaptable, privacy-friendly technology requires minimal training data and quickly adjusts to users.

Keywords:

FMCW artificial neural networks autocorrelation few-shot learning meta-learning radar respiration signal signal processing variational autoencoder vital sign sensing

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Last Updated: Aug 13, 2025

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Published on: March 9, 2018

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Area of Science:

Biomedical Engineering
Signal Processing
Human-Computer Interaction

Background:

Vital signs monitoring is crucial for health assessment but often requires wearables or invasive methods.
Contactless and privacy-preserving methods are needed for continuous health monitoring, especially in shared spaces like offices.

Purpose of the Study:

To develop a radar-based, user-adaptable system for non-contact respiratory signal prediction.
To enable privacy-friendly vital signs estimation in office environments with minimal training data.

Main Methods:

Utilized a 60 GHz frequency-modulated continuous wave radar to collect data from 24 subjects.
Employed episodic optimization with a convolutional variational autoencoder for signal prediction and generalization.
Incorporated autocorrelation analysis to assess and mitigate motion-induced data corruption.

Main Results:

Achieved rapid model adaptation, requiring less than 1-2 seconds for 1-5 training examples.
Demonstrated effective respiratory signal prediction with a contact-free, privacy-friendly approach.
Showcased the system's ability to adjust predictions based on motion corruption levels.

Conclusions:

The proposed radar system offers a novel, rapidly adaptable, non-contact solution for respiratory monitoring in office settings.
This technology minimizes the need for extensive user training and respects user privacy.
The system effectively handles motion artifacts, enhancing the reliability of vital signs estimation.