Non-Contrastive Unsupervised Learning of Physiological Signals from Video

Subtle periodic signals such as blood volume pulse and respiration can be extracted from RGB video, enabling remote health monitoring at low cost. Advancements in remote pulse estimation -- or remote photoplethysmography (rPPG) -- are currently driven by deep learning solutions. However, modern approaches are trained and evaluated on benchmark datasets with associated ground truth from contact-PPG sensors. We present the first non-contrastive unsupervised learning framework for signal regression to break free from the constraints of labelled video data. With minimal assumptions of periodicity and finite bandwidth, our approach is capable of discovering the blood volume pulse directly from unlabelled videos. We find that encouraging sparse power spectra within normal physiological bandlimits and variance over batches of power spectra is sufficient for learning visual features of periodic signals. We perform the first experiments utilizing unlabelled video data not specifically created for rPPG to train robust pulse rate estimators. Given the limited inductive biases and impressive empirical results, the approach is theoretically capable of discovering other periodic signals from video, enabling multiple physiological measurements without the need for ground truth signals. Codes to fully reproduce the experiments are made available along with the paper.Comment: Accepted to CVPR 202

MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation

Visible-light cameras can capture subtle physiological biomarkers without physical contact with the subject. We present the Multi-Site Physiological Monitoring (MSPM) dataset, which is the first dataset collected to support the study of simultaneous camera-based vital signs estimation from multiple locations on the body. MSPM enables research on remote photoplethysmography (rPPG), respiration rate, and pulse transit time (PTT); it contains ground-truth measurements of pulse oximetry (at multiple body locations) and blood pressure using contacting sensors. We provide thorough experiments demonstrating the suitability of MSPM to support research on rPPG, respiration rate, and PTT. Cross-dataset rPPG experiments reveal that MSPM is a challenging yet high quality dataset, with intra-dataset pulse rate mean absolute error (MAE) below 4 beats per minute (BPM), and cross-dataset pulse rate MAE below 2 BPM in certain cases. Respiration experiments find a MAE of 1.09 breaths per minute by extracting motion features from the chest. PTT experiments find that across the pairs of different body sites, there is high correlation between remote PTT and contact-measured PTT, which facilitates the possibility for future camera-based PTT research

Aplicación móvil para medir la frecuencia cardíaca: fiabilidad, barreras y oportunidades

Considering that physical activity is of vital importance in the daily lives of university students, this article outlines the development of an application for Android mobile devices that determines the user’s heart rate using the device’s camera and flash. This application has been made available to the Osiris & Bioaxis Research Group, jointly run by the Faculty of Systems Engineering and the Faculty of Psychology, for their project promoting physical activity and exercise adherence for students of Universidad el Bosque by measuring and tracking that activity. The application allows heart rate measurement by analyzing illuminated images of the user’s finger at a rate of forty frames per second. This study examined the possible limitations on the measurement of heart rate in different versions of the application and compared the reliability of the application with that of an oximeter. It was found that the application has high precision in the results obtained; and, depending on the version of the application, the time taken to output the measurement varies.Teniendo en cuenta que la actividad física es de vital importancia en la vida diaria de los estudiantes universitarios, se detalla el desarrollo de una aplicación para determinar la frecuencia cardíaca para dispositivos móviles Android por medio de la cámara y el flash del dispositivo, la cual está disponible para el grupo de investigación Osiris & Bioaxis de la Facultad de Ingeniería de Sistemas y la Facultad de Psicología, para el proyecto Medición de actividad física y adherencia al ejercicio, con el fin de promover la actividad física y la adherencia por parte de los estudiantes de la Universidad el Bosque.  Esta aplicación permite realizar la medición de la frecuencia cardiaca analizando 40 fotogramas por segundo. Se detallaron las posibles limitantes a la hora de la medición de la frecuencia cardíaca con las diferentes versiones y la fiabilidad de la aplicación en comparación con un oxímetro, estableciendo que el medidor desarrollado posee una alta precisión en los resultados obtenidos y que dependiendo de la versión variara el tiempo que tarda en arrojar el resultado

Single element remote-PPG

\u3cp\u3eCamera-based remote photoplethysmography (remote-PPG) technology has shown great potential for contactless pulse-rate monitoring. However, remote-PPG systems typically analyze face images, which may restrict applications in view of privacy-preserving regulations such as the recently announced General Data Protection Regulation in the European Union. In this paper, we investigate the case of using single-element sensing as an input for remote-PPG extraction, which prohibits facial analysis and thus evades privacy issues. It also improves the efficiency of data storage and transmission. In contrast to known remote-PPG solutions using skin-selection techniques, the input signals in a single-element setup will contain a non-negligible degree of signal components associated with non-skin areas. Current remote-PPG extraction methods based on physiological and optical properties of skin reflections are therefore no longer valid. A new remote-PPG method, named Soft Signature based extraction (SoftSig), is proposed to deal with this situation by softening the dependence of pulse extraction on prior knowledge. A large-scale experiment validates the concept of single-element remote-PPG monitoring and shows the improvement of SoftSig over general purpose solutions.\u3c/p\u3

Single element remote-PPG

Camera-based remote photoplethysmography (remote-PPG) technology has shown great potential for contactless pulse-rate monitoring. However, remote-PPG systems typically analyze face images, which may restrict applications in view of privacy-preserving regulations such as the recently announced General Data Protection Regulation in the European Union. In this paper, we investigate the case of using single-element sensing as an input for remote-PPG extraction, which prohibits facial analysis and thus evades privacy issues. It also improves the efficiency of data storage and transmission. In contrast to known remote-PPG solutions using skin-selection techniques, the input signals in a single-element setup will contain a non-negligible degree of signal components associated with non-skin areas. Current remote-PPG extraction methods based on physiological and optical properties of skin reflections are therefore no longer valid. A new remote-PPG method, named Soft Signature based extraction (SoftSig), is proposed to deal with this situation by softening the dependence of pulse extraction on prior knowledge. A large-scale experiment validates the concept of single-element remote-PPG monitoring and shows the improvement of SoftSig over general purpose solutions