This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind multi-wavelength remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by the design of an alternative rPPG method where a projection plane orthogonal to the skin-tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.

Remote photoplethysmography (rPPG) enables contactless heart-rate monitoring using a regular video camera. Objective: This paper aims to improve the rPPG technology targeting continuous heart-rate measurement during fitness exercises. The fundamental limitation of the existing (multi-wavelength) rPPG methods is that they can suppress at most n − 1 independent distortions by linearly combining n wavelength color channels. Their performance are highly restricted when more than n − 1 independent distortions appear in a measurement, as typically occurs in fitness applications with vigorous body motions. Approach: To mitigate this limitation, we propose an effective yet very simple method that algorithmically extends the number of possibly suppressed distortions without using more wavelengths. Our core idea is to increase the degrees-of-freedom of noise reduction by decomposing the n wavelength camera-signals into multiple orthogonal frequency bands and extracting the pulse-signal per band-basis. This processing, namely Sub-band rPPG (SB), can suppress different distortion-frequencies using independent combinations of color channels. Main results: A challenging fitness benchmark dataset is created, including 25 videos recorded from 7 healthy adult subjects (ages from 25 to 40 yrs; six male and one female) running on a treadmill in an indoor environment. Various practical challenges are simulated in the recordings, such as different skin-tones, light sources, illumination intensities, and exercising modes. The basic form of SB is benchmarked against a state-of-the-art method (POS) on the fitness dataset. Using non-biased parameter settings, the average signal-to-noise-ratio (SNR) for POS varies in [−4.18, −2.07] dB, for SB varies in [−1.08, 4.77] dB. The ANOVA test shows that the improvement of SB over POS is statistically significant for almost all settings (p-value <0.05). Significance: The results suggest that the proposed SB method considerably increases the robustness of heart-rate measurement in challenging fitness applications, and outperforms the state-of-the-art method.

Vital signs monitoring is ubiquitous in clinical environments and emerging in home-based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video-based respiration monitoring method that automatically detects a respiratory region of interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization. We created a benchmark dataset containing 148 video sequences obtained on adults under challenging conditions and also neonates in the neonatal intensive care unit (NICU). The measurements obtained by the proposed video respiration monitoring (VRM) method are not significantly different from the reference methods (guided breathing or contact-based ECG; p -value = 0.6), and explain more than 99% of the variance of the reference values with low limits of agreement (−2.67 to 2.81 bpm). VRM seems to provide a valid solution to ECG in confined motion scenarios, though precision may be reduced for neonates. More studies are needed to validate VRM under challenging recording conditions, including upper-body motion types.

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

Near-infrared (NIR) remote photoplethysmography (PPG) promises attractive applications in darkness, as it involves unobtrusive, invisible light. However, since the PPG strength (AC/DC) is much lower in the NIR spectrum than in the RGB spectrum, robust vital signs monitoring is more challenging. In this paper, we propose a new PPG-extraction method, DIScriminative signature based extraction (DIS), to significantly improve the pulse-rate measurement in NIR. Our core idea is to use both the color signals containing blood absorption variations and additional disturbance signals as input for PPG extraction. By defining a discriminative signature, we use one-step least-squares regression (joint optimization) to retrieve the pulsatile component from color signals and suppress disturbance signals simultaneously. A large-scale lab experiment, recorded in NIR with heavy body motions, shows the significant improvement of DIS over the state-of-the-art method, whereas its principle is simple and generally applicable.

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO₂) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.