A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020

Communication Bandwidth Considerations for Exploration Medical Care During Space Missions

Destinations beyond low Earth orbit, especially Mars, have several important constraints, including limited resupply, limited to no possibility of medical evacuation, and delayed communication with ground support teams. Therefore, medical care is driven towards greater autonomy and necessitates a medical system that supports this paradigm, including the potential for high medical data transfer rates in order to share medical information and coordinate care with the ground in an intermittent fashion as communication allows. The medical data transfer needs for a Martian exploration mission were estimated by defining two medical scenarios that would require high data rate communications between the spacecraft and Earth. One medical scenario involves a case of hydronephrosis (outflow obstruction of the kidney) that evolves into pyelonephritis (kidney infection), then urosepsis (systemic infection originating from the kidney), due to obstruction by a kidney stone. A second medical scenario involved the death of a crewmembers child back on Earth that requires behavioral health care. For each of these scenarios, a data communications timeline was created following the medical care described by the scenario. From these timelines, total medical data transfers and burst transmission rates were estimated. Total data transferred from the vehicle-to-ground were estimated to be 94 gigabytes (GB) and 835 GB for the hydronephrosis and behavioral health scenarios, respectively. Data burst rates were estimated to be 7.7 megabytes per second (MB/s) and 15 MB/s for the hydronephrosis and behavioral health scenarios, respectively. Even though any crewed Mars mission should be capable of functioning autonomously, as long as the possibility of communication between Earth and Mars exists, Earth-based subject matter experts will be relied upon to augment mission medical capability. Therefore, setting an upper boundary limit for medical communication rates can help factor medical system needs into total vehicle communication requirements

3D + time blood flow mapping using SPIM-microPIV in the developing zebrafish heart

We present SPIM-μPIV as a flow imaging system, capable of measuring in vivo flow information with 3D micron-scale resolution. Our system was validated using a phantom experiment consisting of a flow of beads in a 50 μm diameter FEP tube. Then, with the help of optical gating techniques, we obtained 3D + time flow fields throughout the full heartbeat in a ∼3 day old zebrafish larva using fluorescent red blood cells as tracer particles. From this we were able to recover 3D flow fields at 31 separate phases in the heartbeat. From our measurements of this specimen, we found the net pumped blood volume through the atrium to be 0.239 nL per beat. SPIM-μPIV enables high quality in vivo measurements of flow fields that will be valuable for studies of heart function and fluid-structure interaction in a range of small-animal models

Assessment of pulmonary edema: principles and practice

Pulmonary edema increasingly is recognized as a perioperative complication affecting outcome. Several risk factors have been identified, including those of cardiogenic origin, such as heart failure or excessive fluid administration, and those related to increased pulmonary capillary permeability secondary to inflammatory mediators. Effective treatment requires prompt diagnosis and early intervention. Consequently, over the past 2 centuries a concentrated effort to develop clinical tools to rapidly diagnose pulmonary edema and track response to treatment has occurred. The ideal properties of such a tool would include high sensitivity and specificity, easy availability, and the ability to diagnose early accumulation of lung water before the development of the full clinical presentation. In addition, clinicians highly value the ability to precisely quantify extravascular lung water accumulation and differentiate hydrostatic from high permeability etiologies of pulmonary edema. In this review, advances in understanding the physiology of extravascular lung water accumulation in health and in disease and the various mechanisms that protect against the development of pulmonary edema under physiologic conditions are discussed. In addition, the various bedside modalities available to diagnose early accumulation of extravascular lung water and pulmonary edema, including chest auscultation, chest roentgenography, lung ultrasonography, and transpulmonary thermodilution, are examined. Furthermore, advantages and limitations of these methods for the operating room and intensive care unit that are critical for proper modality selection in each individual case are explored

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform