The present invention relates to a system, method and corresponding computer program for milk ejection reflex (MER) determination, the system comprising a breast shield arrangement (1) for a breast pump (2) configured to be attached on a first breast of a female, a physiological sensor unit (4) for receiving a physiological reception signal from the second breast, which is opposite to the first breast, wherein the physiological reception signal is indicative of fluid contents in the second breast, and wherein the system is configured to determine a milk ejection reflex based on a change in fluid contents in the second breast. It finds particular application during and in connection with breastfeeding and allows for direct detection of the beginning of the milk flow in the breast, i.e. the occurrence of the MER, without delay and with a high degree of precision.

Scalable, configurable, complete spectrum universal health metrics monitors and bicorders are disclosed that record data or make selected determinations from a complete spectrum of health determinations regarding or utilizing sensor observations of people. Universal health metrics monitors utilize necessary resources and predetermined criteria in their making of selected health determinations. Universal health metrics monitors may utilize measure points in their locating of selected analytically rich aspects, characteristics, or features of or from sensor observation-derived representations, Universal health metrics monitors assign appropriate informational representations to selected analytically rich aspects, characteristics, features, or measure points, which are stored in datasets where they can be utilized in real-time or thereafter by universal health metrics monitors in their making of selected health determinations regarding or utilizing sensor observations or people who are subjects of sensor observations.

Systems and methods are provided involving various medical monitoring and/or diagnostic systems. The monitoring and diagnostic systems may involve one or more connected devices (e.g., a smart watch and/or other sensor device) and may continuously monitor an individual and analyze physiological and other data to determine a medical device, condition or event has occurred. The monitoring and diagnostic systems may be a guided self-examination system for determining a medical diagnosis, condition or event. The medical monitoring and diagnostic systems may even be specific to a family or individuals in a certain geographic location.

Circadian health recommendations and/or automation instructions and spectral data on which they are based can be provided to a user through a mobile device that lacks a spectral sensor. A user mobile device lacking a spectral data uploads place and time data to a cloud-based circadian health system. A light-exposure model of the circadian health system estimates spectral data values based on the place and time data. The light-exposure model’s estimation can be based on data received by the circadian health system based on user devices equipped with spectral sensors and from other sources. A relatively small number of mobile user devices (with spectral sensors) can thus provide for spectral value estimates for a relatively large population of mobile user devices that lack spectral sensors—greatly expanding the range and number of people that benefit from improved circadian health.

A wearable device is provided. The wearable device includes a memory configured to store instructions, at least one display having a display area, a frame configured to support the at least one display, the frame including a nose pad in contact with a part of a user's body wearing the wearable device, a photoplethysmography (PPG) sensor exposed through at least a portion of the frame in contact with other part of the user's body, at least one microphone disposed in the nose pad, and a processor. The processor, when executing the instructions, is configured to identify a breathing state of the user, based at least in part on first data acquired through the PPG sensor and second data acquired through the at least one microphone.

Technologies are provided for remote neuropsychological assessments and other types of remote assessments (medical or otherwise).

A system for measuring health data includes a measurement device. The measurement device includes at least one measurement interface to receive a first fluid sample, a processor to measure one or more first characteristics of the first fluid sample, and at least one memory device to store first data. The processor reads the first data and measures the one or more first characteristics of the first fluid sample according to the first data. The at least one memory device also stores second data. The processor reads the second data instead of the first data to reconfigure the measurement device and measures one or more second characteristics of a second fluid sample according to the second data. An external processing device may be communicatively coupled to the measurement device and may execute a healthcare application that communicates with the measurement device and may be employed to reconfigure the measurement device.

A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

The physical and physiological events at one end of an Abreu Brain Thermal Tunnel (ABTT) are reproduced at the opposite end. Thus, the ABTT enables the direct transfer of outputs from a brain core to an ABTT terminus without significant barriers. Accordingly, apparatuses, systems, devices, mechanisms, and methods use the ABTT terminus and the ABTT to measure the temperature of the brain core.