For Heart Rate Assessments from Drone Footage in Disaster Scenarios.

The ability to use drones to obtain important vital signs could be very valuable for emergency personnel during mass-casualty incidents. The rapid and robust remote assessment of heart rates could serve as a life-saving decision aid for first-responders. With the flight sensor data of a specialized drone, a pipeline was developed to achieve a robust, non-contact assessment of heart rates through remote photoplethysmography (rPPG). This robust assessment was achieved through adaptive face-aware exposure and comprehensive de-noising of a large number of predicted noise sources. In addition, we performed a proof-of-concept study that involved 18 stationary subjects with clean skin and 36 recordings of their vital signs, using the developed pipeline in outdoor conditions. In this study, we could achieve a single-value heart-rate assessment with an overall root-mean-squared error of 14.3 beats-per-minute, demonstrating the basic feasibility of our approach. However, further research is needed to verify the applicability of our approach in actual disaster situations, where remote photoplethysmography readings could be impacted by other factors, such as blood, dirt, and body positioning.

Multisensor data fusion for enhanced respiratory rate estimation in thermal videos.

Scientific studies have demonstrated that an atypical respiratory rate (RR) is frequently one of the earliest and major indicators of physiological distress. However, it is also described in the literature as "the neglected vital parameter", mainly due to shortcomings of clinical available monitoring techniques, which require attachment of sensors to the patient's body. The current paper introduces a novel approach that uses multisensor data fusion for an enhanced RR estimation in thermal videos. It considers not only the temperature variation around nostrils and mouth, but the upward and downward movement of both shoulders. In order to analyze the performance of our approach, two experiments were carried out on five healthy candidates. While during phase A, the subjects breathed normally, during phase B they simulated different breathing patterns. Thoracic effort was the gold standard elected to validate our algorithm. Our results show an excellent agreement between infrared thermography (IRT) and ground truth. While in phase A a mean correlation of 0.983 and a root-mean-square error of 0.240 bpm (breaths per minute) was obtained, in phase B they hovered around 0.995 and 0.890 bpm, respectively. In sum, IRT may be a promising clinical alternative to conventional sensors. Additionally, multisensor data fusion contributes to an enhancement of RR estimation and robustness.

Robust remote monitoring of breathing function by using infrared thermography.

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.