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Open AccessArticle

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

1
Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland
2
Artificial Intelligence Products Group, Intel Corporation, 12220 Scripps Summit Dr, San Diego, CA 92131, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4405; https://doi.org/10.3390/app9204405
Received: 15 September 2019 / Revised: 8 October 2019 / Accepted: 9 October 2019 / Published: 17 October 2019
(This article belongs to the Special Issue Contactless Vital Signs Monitoring)
Estimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep Neural Networks (DNN). To perform extensive benchmark evaluation, we acquired two thermal datasets using FLIR® cameras with a spatial resolution of 80 × 60 and 320 × 240 from 71 volunteers in total. In-depth analysis of the proposed Convolutional-based Super Resolution model showed that for images downscaled with a factor of 2 and then super-resolved using Deep Learning (DL) can lead to better RR estimation accuracy than from original high-resolution sequences. In addition, if an estimator based on a dominating peak in the frequency domain is used, SR can outperform original data for a down-scale factor of 4 and images as small as 20 × 15 pixels. Our study also showed that RR estimation accuracy is better for super-resolved data than for images with color changes magnified using algorithms previously applied in the literature for enhancing vital signs patterns. View Full-Text
Keywords: respiratory rate; remote medical diagnostics; vital signs estimation; super resolution; deep learning; convolutional neural networks respiratory rate; remote medical diagnostics; vital signs estimation; super resolution; deep learning; convolutional neural networks
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Kwasniewska, A.; Ruminski, J.; Szankin, M. Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks. Appl. Sci. 2019, 9, 4405.

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