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10.3390/s21134406 http://scihub22266oqcxt.onion/10.3390/s21134406 34199084!8271612!34199084     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2021 ; 21 (13): ä Nephropedia Template TP {{tp|p=34199084|t=2021. Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index |pdf=|usr=}}{{34199084}} gab.com Text Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34199084 #FOAvip Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method. Twit Text FOAVip Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34199084 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34199084 #FOAvip English Wikipedia <ref>{{Cite pmid|34199084}}</ref> Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index #MMPMID34199084 Takahashi Y; Gu Y; Nakada T; Abe R; Nakaguchi T Sensors (Basel) 2021[Jun]; 21 (13): ä PMID34199084show ga Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method. |*COVID-19[MESH] |*Respiratory Rate[MESH] |Algorithms[MESH] |Humans[MESH] |Monitoring, Physiologic[MESH] |Pandemics[MESH] |Respiration[MESH] |SARS-CoV-2[MESH]Estimation of Respiratory Rate from Thermography Using Respiratory Lik(epmc).pdf DeepDyve Pubget Overpricing