No abstract

This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component-the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.

No abstract

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images-referred to as ICLIP images-yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks.