Key Vital Signs Monitor Based on MIMO Radar

Gottinger, Michael; Notari, Nicola; Dutler, Samuel; Kranz, Samuel; Vetsch, Robin; Pittorino, Tindaro; Würsch, Christoph; Piai, Guido

doi:10.3390/s25134081

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Open AccessArticle

Key Vital Signs Monitor Based on MIMO Radar

by

Michael Gottinger

^*

,

Nicola Notari

,

Samuel Dutler

,

Samuel Kranz

,

Robin Vetsch

,

Tindaro Pittorino

,

Christoph Würsch

and

Guido Piai

ESA Institute for Electronics, Sensorics and Actorics, Ostschweizer Fachhochschule, 9001 St. Gallen, CH, Switzerland

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(13), 4081; https://doi.org/10.3390/s25134081

Submission received: 16 May 2025 / Revised: 26 June 2025 / Accepted: 27 June 2025 / Published: 30 June 2025

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

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Abstract

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute.

Keywords: computer-aided diagnosis; convolutional neural networks; displacement measurements; machine learning; MIMO radar; patient monitoring; sleep apnea

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MDPI and ACS Style

Gottinger, M.; Notari, N.; Dutler, S.; Kranz, S.; Vetsch, R.; Pittorino, T.; Würsch, C.; Piai, G. Key Vital Signs Monitor Based on MIMO Radar. Sensors 2025, 25, 4081. https://doi.org/10.3390/s25134081

AMA Style

Gottinger M, Notari N, Dutler S, Kranz S, Vetsch R, Pittorino T, Würsch C, Piai G. Key Vital Signs Monitor Based on MIMO Radar. Sensors. 2025; 25(13):4081. https://doi.org/10.3390/s25134081

Chicago/Turabian Style

Gottinger, Michael, Nicola Notari, Samuel Dutler, Samuel Kranz, Robin Vetsch, Tindaro Pittorino, Christoph Würsch, and Guido Piai. 2025. "Key Vital Signs Monitor Based on MIMO Radar" Sensors 25, no. 13: 4081. https://doi.org/10.3390/s25134081

APA Style

Gottinger, M., Notari, N., Dutler, S., Kranz, S., Vetsch, R., Pittorino, T., Würsch, C., & Piai, G. (2025). Key Vital Signs Monitor Based on MIMO Radar. Sensors, 25(13), 4081. https://doi.org/10.3390/s25134081

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