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.

A device to hold multiple blood pressure reading units with position indicator has a blood pressure monitor holder with a shelf portion holding multiple blood pressure monitors. The holder is moveably attached using a position indicator attached to the patient using a collar. The position indicator is a flexible ruler that shows the position of the units below the collar. The position indicator attaches to the comfortable adjustable collar that attaches to a patient's neck and is retractably positioned to indicate the position of the reading to be taken. If there is a significant difference between the left and right readings, then further tests are ordered. This establishes a repeatable baseline where further readings are taken from the same position. The location of the reading can provide important diagnosis information at the primary care level long before symptoms and complications begin.

Methods and apparatuses, including devices and systems, for remote and detection and/or diagnosis of acute myocardial infarction (AMI). In particular, described herein are handheld devices having an electrode configuration capable of recording three orthogonal ECG lead signals in an orientation-specific manner, and transmitting these signals to a processor. The processor may be remote or local, and it may automatically or semi-automatically detect AMI, atrial fibrillation or other heart disorders based on the analyses of the deviation of the recorded 3 cardiac signals with respect to previously stored baseline recordings.

A removable smartphone case is disclosed. The removable smartphone case includes a case body configured to receive a smartphone, a radio frequency (RF) front-end connected to the case body and including a semiconductor substrate and an antenna array including at least one transmit antenna configured to transmit radio waves below the skin surface of a person and a two-dimensional array of receive antennas configured to receive radio waves, the received radio waves including a reflected portion of the transmitted radio waves, wherein the semiconductor substrate includes circuits configured to generate signals in response to the received radio waves, a communications interface connected to the case body and configured to transmit digital data that corresponds to the signals generated by the semiconductor substrate from the removable smartphone case, and an alignment feature integrated into the case body and configured to align the antenna array with an object.

An ophthalmic surgical system includes an imaging unit configured to generate a fundus image of an eye and a depth imaging system configured to generate a depth-resolved image of the eye. The system further includes a tracking system communicatively coupled to the imaging unit and depth imaging system, the tracking system comprising a processor and memory configured to analyze the fundus image generated by the imaging unit to determine a location of a distal tip of a surgical instrument in the fundus image, analyze the depth-resolved image generated by the depth imaging system to determine a distance between the distal tip of the surgical instrument and a retina of the eye, generate a visual indicator to overlay a portion of the fundus image, the visual indicator indicating the determined distance between the distal tip and the retina, modify the visual indicator to track a change in the location of the distal tip within the fundus image in real-time, and modify the visual indicator to indicate a change in the distance between the distal tip of the surgical instrument and the retina in real-time.

One embodiment relates to an apparatus, comprising logic, at least partially incorporated into hardware, to: receive first sensor data associated with a first sensor of a first smart device; determine a first reliability factor associated with the first sensor data; receive second sensor data associated with a second sensor of a second smart device; and determine a second reliability factor associated with the second sensor data. The logic is further to determine a sensor data reporting plan based upon the first reliability factor and the second reliability factor, the sensor data reporting plan indicating whether each of the first sensor and the second sensor are to subsequently send their respective sensor data to a primary communication device.

A method includes placing a set of electrodes on a body surface of a patient's body. The method also includes digitizing locations for the electrodes across the body surface based on one or more image frames using range imaging and/or monoscopic imaging. The method also includes estimating locations for hidden ones of the electrodes on the body surface not visible during the range imaging and/or monoscopic imaging. The method also includes registering the location for the electrodes on the body surface with predetermined geometry information that includes the body surface and an anatomical envelope within the patient's body. The method also includes storing geometry data in non-transitory memory based on the registration to define spatial relationships between the electrodes and the anatomical envelope.

In some aspects, a computer-implemented method is disclosed for gathering and processing sensor data to identify a risk of impacting or causing a skin injury. The computer-implemented method can include: receiving, via a computer network, sensor feature data representing output of a user sensor configured to be worn on a limb of a user; generating activity classification model output data using the sensor feature data and an activity classification model, the activity classification model output data representing likelihoods that the sensor feature data corresponds to each of a plurality of different activity classifications; determining an activity classification from the activity classification model output data; and transmitting, via the computer network, display data representing the activity classification to a computing device configured to present the display data.

A variation of a method for collecting and processing bioelectrical signals includes: establishing bioelectrical contact between a user and one or more sensors of a biomonitoring neuroheadset; monitoring contact characteristics of the one or more sensors based on bioelectrical signals detected at the one or more sensors; and providing feedback to the user based on the contact characteristics. A variation of a system for collecting and processing bioelectrical signals includes a set of sensors (e.g., electrodes) and a processing subsystem configured process the set of bioelectrical signals.

A device includes a housing, an emitter, a detector, and a processor. The housing has a body contact surface. The emitter is coupled to the housing and has an emission surface and has an electrical terminal. The emission surface is configured to emit light proximate the body contact surface in response to a signal applied to the electrical terminal. The detector is coupled to the housing. The detector has a sense surface and an output terminal. The detector is configured to provide an output signal on the output terminal in response to light detected at the sensor surface. The processor is configured to implement an algorithm to determine a tissue site based on the emitted light and based on the detected light.