Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging

The ability to monitor respiratory rate is extremely important for medical treatment, healthcare and fitness sectors. In many situations, mobile methods, which allow users to undertake every day activities, are required. However, current monitoring systems can be obtrusive, requiring users to wear respiration belts or nasal probes. Recent advances in thermographic systems have shrunk their size, weight and cost, to the point where it is possible to create smart-phone based respiration rate monitoring devices that are not affected by lighting conditions. However, mobile thermal imaging is challenged in scenes with high thermal dynamic ranges. This challenge is further amplified by general problems such as motion artifacts and low spatial resolution, leading to unreliable breathing signals. In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes. It has three main contributions. The first is a novel Optimal Quantization technique which adaptively constructs a color mapping of absolute temperature to improve segmentation, classification and tracking. The second is the Thermal Gradient Flow method that computes thermal gradient magnitude maps to enhance accuracy of the nostril region tracking. Finally, we introduce the Thermal Voxel method to increase the reliability of the captured respiration signals compared to the traditional averaging method. We demonstrate the extreme robustness of our system to track the nostril-region and measure the respiratory rate in high dynamic range scenes.Comment: Vol. 8, No. 10, 1 Oct 2017, Biomedical Optics Express 4480 - Full abstract can be found in this journal article (due to limited word counts of 'arXiv abstract'

Non-invasive respiration monitoring by thermal imaging to detect sleep apnoea

Accurate airflow measurements are vital to diagnose apnoeas; respiratory pauses occurring during sleep that interrupt airflow to the lungs. Apnoea diagnosis usually requires an overnight polysomnography during which numerous vital signs are monitored, including respiratory rate and airflow. The current gold standard in respiration monitoring is a nasal pressure sensor which is placed inside the nostrils of the patient and through which the airflow is measured. Due to the contact nature of the sensor, it is often refused or removed during polysomnography, especially in the case of paediatric patients. We have found that around 50% of children refuse the use of nasal prongs due to its in-vasiveness, and of those that accepted it, 64% removed the sensor over the course of the polysomnography. We evaluated a non-contact method to monitor respiration by developing infrared thermal imaging, whereby temperature fluc-tuations associated with respiration are measured and correlated with airflow. A study was carried out on a sample of 11 healthy adult volunteers whose res-piratory signals were recorded over four simulated apnoea scenarios. The res-piratory signal obtained through thermal imaging was compared against the gold standard nasal pressure sensor. In 70% of cases, apnoea related events were well correlated with airflow sensor readings. In 16% of recordings the subject’s head position did not allow correct identification of the region of interest (i.e. the nostrils). For the remaining 14% of cases there was partial agreement between the thermal measurements and airflow sensor readings. These results indicate thermal imaging can be valuable as a detection tool for sleep apnoea, particularly in the case of paediatric patients

Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous light source (e.g., infrared light emitting diode) and sensor (e.g., photodetector). This light-wave sensing (LWS) system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range. The anomaly detection model demonstrates up to 96.6% average accuracy in classifying 7 different types of breathing data using machine learning. The model can also detect faulty data collected by the system that does not contain breathing information. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.Comment: 5 pages, 4 figures, submitted to IEEE conferenc

VOLUNTARY CONTROL OF BREATHING ACCORDING TO THE BREATHING PATTERN DURING LISTENING TO MUSIC AND NON-CONTACT MEASUREMENT OF HEART RATE AND RESPIRATION

We investigated if listening to songs changes breathing pattern which changes autonomic responses such as heart rate (HR) and heart rate variability (HRV) or change in breathing pattern is a byproduct of listening to songs or change in breathing pattern as well as listening to songs causes changes in autonomic responses. Seven subjects (4 males and 3 females) participated in a pilot study where they listened to two types of songs and used a custom developed biofeedback program to control their breathing pattern to match the one recorded during listening to the songs. Coherencies between EEG, breathing pattern and RR intervals (RRI) were calculated to study the interaction with neural responses. Trends in HRV varied only during listening to songs, suggesting that autonomic response was affected by listening to songs irrespective of control of breathing. Effective coherence during songs while spontaneously breathing was more than during silence and during control of breathing. These results, although preliminary, suggest that listening to songs as well as change in breathing patterns changes the autonomic response but the effect of listening to songs may surpass the effect of changes in breathing. We explored feasibility of using non-contact measurements of HR and breathing rate (BR) by using recently developed Facemesh and other methods for tracking regions of interests from videos of faces of subjects. Performance was better for BR than HR, and over currently used methods. However, refinement of the approach would be needed to get the precision required for detecting subtle changes