Google Inc.

Verily Life Sciences LLC

Rochester Institute of Technology

Brian Derek DeBusschere

Jeffrey L Rogers

James Moad Reid

Shannon Colleen Fong

Eric McCallister Bennett

David Borkholder

Apparatuses, machine-accessible storage media, and methods for noninvasively measuring a heart performance metric, such as left ventricular filling pressure, are described. In an embodiment, the apparatus includes a housing shaped to be grasped by a hand; a tower protruding from a surface of the housing positioned to conform to a finger of the hand when grasping the housing; a tactile sensor disposed on a curved surface of the tower and adapted to measure blood pressure pulsatility in a digital artery of the finger and output pulsatility signals indicative of the blood pressure pulsatility; a graphical user interface for orchestrating a test of the performance metric of the heart and displaying results of the test.

Hand-held apparatus for noninvasive measurement of a heart performance metric

This document describes techniques for, and systems that enable, in-ear health monitoring. The techniques described herein enable early detection of health conditions (e.g., contagious disease) through use of an in-ear health-monitoring and audio device. These techniques prompt a user, often through the user's smart phone, to listen to audio content through the device, which also takes the user's temperature. Through repetitive use, the techniques are capable of determining a temperature differential for the user, which aids in early detection of a contagious disease or other malady.

In-ear health monitoring

This document describes automated nursing assessments. Automation of the nursing assessment involves a nursing-assessment device that makes determinations of a person's mood, physical state, psychosocial state, and neurological state. To determine a mood and physical state of a person, video of the person is captured while the person is positioned in front of an everyday object, such as a mirror. The captured video is then processed according to human condition recognition techniques, which produces indications of the person's mood and physical state, such as whether the person is happy, sad, healthy, sick, vital signs, and so on. In addition to mood and physical state, the person's psychosocial and neurological state are also determined. To do so, questions are asked of the person. These questions are determined from a plurality of psychosocial and neurological state assessment questions, which include queries regarding how the person feels, what the person has been doing, and so on. The determined questions are asked through audible or visual interfaces of the nursing-assessment device. The person's responses are then analyzed. The analysis involves processing the received answers according to psychosocial and neurological state assessment techniques to produce indications of the person's psychosocial and neurological state.

Automated nursing assessment

This document describes optical central venous pressure measurement. To determine the central venous pressure (CVP) of a person optically, video of a right side of the person's neck is captured. By way of example, a medical professional records a video of the right side of the person's neck using a smartphone. The right side of the person's neck is captured because it is where the person's external and internal jugular veins are located and pulsatile motions that are usable to measure CVP occur in those veins. The video is then processed according to video motion amplification techniques to generate a reconstructed video of the right side of the person's neck. In the reconstructed video, the pulsatile motion of the person's venous system that occurs at the right side of their neck is visually amplified. Using the reconstructed video, measurements are made of a distance between a peak of the visually-amplified pulsatile motion and an anatomical feature of the person. The measured distance between the peak of the visually-amplified pulsatile motion and the anatomical feature is used to determine CVP of the person. These techniques enable CVP to be determined without relying on estimates made by medical professionals.

Optical central venous pressure measurement

This document describes assessing cardiovascular function using an optical sensor, such as through sensing relevant hemodynamics understood by pulse transit times, blood pressures, pulse-wave velocities, and, in more breadth, ballistocardiograms and pressure-volume loops. The techniques disclosed in this document use various optical sensors to sense hemodynamics, such as skin color and skin and other organ displacement. These optical sensors require little if any risk to the patient and are simple and easy for the patient to use.

Assessing cardiovascular function using an optical sensor

This document describes automated abdominojugular reflux (AJR) testing. To automate AJR tests, a pressure cuff wrapped around a person's abdomen applies pressure while video of their neck is captured. By way of example, a medical professional wraps a pressure cuff around the person's abdomen and records video of the person's neck using a smartphone, which communicates with the pressure cuff to synchronize the application of pressure with video capture. The video is processed to detect and track the response of jugular venous pulse (JVP), which is compared to AJR test thresholds to determine test results. While determining JVP, and thereby results of AJR tests, from reconstructed videos may not result in data that is as accurate as invasive intra-heart tests, it requires little if any risk to patients and is easy for medical professionals to perform. Further, these techniques enable AJR tests to be performed automatically and without relying on estimates made by skilled medical professionals.

Automated abdominojugular reflux testing

A method, non-transitory computer readable medium, and apparatus that includes obtaining, by a dosimetry computing device, sensor readings from at least one sensor. An event is identified, by the dosimetry computing device, based on at least one of one or more of the obtained sensor readings or one or more determinations based on the obtained sensor readings meeting one or more selection. At least one of the one or more determinations or the sensor readings which meet one or more of the selection criteria when the event is identified is stored by the dosimetry computing device.

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Los Gatos, CA, United States of America

Brian Derek DeBusschere