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Altered breathing rate is an important sign of disease status. Currently used machine-based monitoring of the breathing rate includes contact with the body, which may result in pain and discomfort. In this paper, a non-contact breathing monitoring technique is proposed by integrating RGB and thermal imaging systems with RGB-thermal image registration. This method provides a linear mapping for automated selection of the region of interest (ROI) followed by tracking to extract the breathing rate. To evaluate the efficacy of the proposed approach and its robustness against motion, talking, varying breathing rate or rhythm, and high ambient temperature, this study was conducted in three phases. Validation of the proposed approach demonstrated a strong agreement with the reference method of breathing rate monitoring using a respiration belt. During normal breathing, the mean absolute error (MAE) reached 0.11 bpm (breaths per minute). While in more challenging conditions, defined by three phases, the MAE reached 1.46, 2.08, and 1.69 bpm, respectively. In short, the proposed method performance is a promising alternative to a contact-based method due to its strong agreement and might be useful in diverse applications such as sport studies, rehabilitation centres, quarantine centres, and in hospital or airport screening during the COVID 19 pandemic. [ABSTRACT FROM AUTHOR]

This paper presents a method of screen-printing a conductive carbon nanotube printing paste onto textile substrates to prepare textronic strain sensors for measuring breathing rate. Screen-printed sensors can be integrated with garments after construction and do not require significant modification of the construction process. Personalisation of the garment is used to optimize sensor placement for improved measurement accuracy. Changes in the electrical resistance of the sensor as a result of changes in strain are detected and used to determine the breathing rate. [ABSTRACT FROM AUTHOR]

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data. [ABSTRACT FROM AUTHOR]