An abnormal breathing rate (BR) is one of the strongest markers of physiological distress. Moreover, it plays an important role in early detection of sudden infant death syndrome, as well as in the diagnosis of respiratory disorders. However, the current measuring modalities can cause discomfort to the patient, since attachment to the patient's body is required. This paper proposes a new approach based on infrared thermography to remotely monitor BR. This method allows to (1) detect automatically the nose, (2) track the associate region of interest (ROI), and (3) extract BR. To evaluate the performance of this method, thermal recording of 5 healthy subjects were acquired. Results were compared with BR obtained by capnography. The introduced approach demonstrated an excellent performance. ROIs were precisely segmented and tracked. Furthermore, a Bland-Altman diagram showed a good agreement between estimated BR and gold standard. The mean correlation and mean absolute BR error are 0.92 ± 0.07 and 0.53 bpm, respectively. In summary, infrared thermography seems to be a great, clinically relevant alternative to attached sensors, due to its outstanding characteristics and performance.
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http://dx.doi.org/10.1109/EMBC.2015.7319333 | DOI Listing |
Front Sports Act Living
December 2024
Department of Sports Science and Movement Pedagogy, Technische Universität Braunschweig, Braunschweig, Germany.
Introduction: Monitoring internal load is crucial for athletes but often requires invasive methods for muscle-related parameters, limiting practicality. Infrared thermography (IRT) related parameters might overcome this limitation. This systematic review aimed to examine the available literature on the response of IRT related parameters to (non-)sport specific exercise and reveal relationships with internal load parameters in athletic populations.
View Article and Find Full Text PDFSci Rep
December 2024
School of Agricultural Science (FCA), Federal University of Grande Dourados (UFGD), Dourados, MS, 79824-900, Brazil.
The aim of this study was to evaluate the effects of two styles of classical music, based on different tempos (BPM), on the physiological and blood parameters of horses during social isolation and restriction of movements. First experiment was carried out using nine horses of no defined breed, distributed in Control, Slow-tempo music and Moderate-tempo music .For social isolation and restriction of movement, the animals were housed daily in individual stalls for two hours and exposed to the stimuli for 60 min, and eye temperature, heart rate, and respiratory rate were assessed.
View Article and Find Full Text PDFVet Sci
December 2024
Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece.
This study aimed to investigate the incidence of subclinical mastitis (SCM), the implicated pathogens, and their impact on milk quality in dairy sheep in Greece. Furthermore, we preliminarily evaluated infrared thermography and the application of AI tools for the early, non-invasive diagnosis of relevant cases. In total, 660 milk samples and over 2000 infrared thermography images were obtained from 330 phenotypically healthy ewes.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Collaborative Innovation Center for Nanomaterials & Devices, College of Physics, Qingdao University, Qingdao 266071, China.
Irregularly shaped wounds cause severe chronic infections, which have attracted worldwide attention due to their high prevalence and poor treatment outcomes. In this study, we designed a new composite functional dressing consisting of traditional Chinese herb carbonized plant powder (CPP) and a polyacrylic acid (PAA)/polyethylenimine (PEI) gel. The rapid gelation of the dressing within 6-8 s allowed the gel to be firmly attached to an irregularly shaped wound surface and avoided powder detachment.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Sta. Catarina Martir, San Andrés Cholula 72810, Mexico.
Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images.
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