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. 2023 Sep 5;23(18):7665.
doi: 10.3390/s23187665.

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Emad Arasteh  1   2 , Esther S Veldhoen  3 , Xi Long  4 , Maartje van Poppel  3 , Marjolein van der Linden  1 , Thomas Alderliesten  1 , Joppe Nijman  3 , Robbin de Goederen  1 , Jeroen Dudink  1
Affiliations

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

Emad Arasteh et al. Sensors (Basel). .

Abstract

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

Keywords: UWB radar; early childhood; heart rate; monitor; respiratory rate.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic general overview of sleep stage classification steps in this research.
Figure 2
Figure 2
Movement computed from radar and the corresponding video signal intensity and frames. (A) Movement computed and depicted for 3 min of radar data. (B) The specified time samples for movement. (C) The corresponding grayscale intensity (normalized magnitude as pixel value subtracted by average value divided by standard deviation) in the specific time samples of video. (D) Three frames before, during, and after clutter (nontarget person) presence. For privacy issues, the face of the patient has been blocked.
Figure 3
Figure 3
(A) Example of a 10 s spectrogram for one estimated tag. (B) The VGG-16 trained network architecture in this study (from [23]).
Figure 4
Figure 4
Results for RR estimation of 10 folds. (A) “True” versus “Estimated” and (B) Bland–Altman plot (blue lines are the limits of agreement (LOA)).
Figure 5
Figure 5
Results for HR estimation of 10 folds. (A) “True” versus “Estimated” and (B) Bland–Altman plot (blue lines are LOA).
See this image and copyright information in PMC

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