A monitoring system for monitoring a biological subject including a monitoring device having a housing configured to be attached to or supported by an ear of the subject in use, one or more sensors, the one or more sensors including a photoplethysmogram (PPG) sensor provided in the housing and configured to measure attributes of blood flow within the ear, and a monitoring device processor configured to acquire sensors signals from the one or more sensors and generate sensor data at least partially in accordance with signals from the one or more sensors. A transmitter is provided that transmit the sensor data with one or more processing systems receiving the sensor data, analyzing the sensor data and generating a health state indicator indicative of a health state of the subject.

Provided is a method for controlling a mask apparatus. The method for controlling the mask apparatus includes measuring a current pressure value with respect to a mask by using a pressure sensor, comparing each of a preset atmospheric pressure maximum estimation and a preset and a preset atmospheric pressure minimum estimation to the current pressure value; updating the atmospheric pressure maximum estimation and the atmospheric pressure minimum estimation according to the comparison result, and controlling a voice output of a speaker based on a difference between the updated atmospheric pressure maximum estimation and the updated atmospheric pressure minimum estimation.

A method comprising receiving an input indicating intake information associated with a patient. Based on the input, the method further includes determining an initial discharge date and receiving mobility information associated with the patient. Based in part on the mobility information, the method further includes determining an estimated discharge date and a confidence metric associated with the estimated discharge date, determining that the estimated discharge date is later than the initial discharge date by more than a threshold period of time, and determining that the confidence metric is greater than a threshold metric. Based in part on the estimated discharge date being later than the initial discharge date by more than the threshold period of time and the confidence metric being greater than the threshold metric, the method further includes generating an alert.

A method includes receiving a plurality of data points including electrical activation (EA) values measured at respective positions in at least a portion of a surface of a cardiac chamber of a heart of a patient. Using a predefined EA value criterion, the EA values in a given region of the cardiac surface are classified into multiple distinct EA wave-fronts, and multiple layers of EA values are calculated for the given region, wherein each EA layer includes the EA values found to belong to a respective and contiguous EA wave-front. The multiple EA layers are overlayed on a graphical representation of the surface. The graphical representation with the multiple overlaid EA layers is displayed to a user, with a graphical indication distinguishing between the multiple EA layers.

Automated detection of head and/or head-affecting body impact events in data collected is performed using instrumented mouthguard devices. For example, in some embodiments the present disclosure relates to training and operation of an impact classifier system, which is configured to identify head affecting impacts from time-series data collected by an instrumented mouthguard device. Some embodiments relate to a two-stage method for processing impacts, including a first stage in which a set of data is classified by such an impact classifier system, and a second stage whereby impacts classified as head affecting impacts are designated a numerical value based on a predefined scale.

Various methods and systems are provided for analyzing an electrocardiogram (ECG) in real-time using machine learning to identify heartbeats, calculate a cycle length for each heartbeat, and display the cycle length for each heartbeat at a user interface. Waveform morphology of ECG data is continuously learned to identify recurrent signals and generate templates based on recurrent signals, to which ECG data is compared to identify and display heartbeats. Generated templates are continuously updated to reflect changing waveform morphologies.

A health monitoring urinalysis system that monitors several health indicators from human urine is provided. The system includes a temperature sensor and several electrochemical sensors that are installed in a urine collection basin, such as, a toilet or a urinal and may automatically collect urine information after each use. The system collects data during the routine and normal use of a urine collection basin. The system includes a control and measurement unit that may be installed outside the urine collection basin. The control and measurement unit receives sensor measurements and transmits the measurements to one or more remote electronic devices. The remote electronic devices and/or the processor of the control and measurement unit perform data analysis, provide diagnostic, and generate health alerts. The system performs recurrent health monitoring after the urine collection basin is used and may detect abnormal conditions at early stages, in addition to a routine urine test.

An ECG monitoring device includes a vibration meter sensor unit including at least one vibration meter sensor attached to an instrument at which a person to be observed is positioned, and configured to acquire a vibration signal by detecting a vibration transmitted through the instrument in a non-contact or non-invasive method, a filter unit configured to extract a seismocardiography signal (“SCG signal”) generated by a heart vibration of the person to be observed by receiving the vibration signal and filtering a predetermined frequency band from the received vibration signal, and an ECG waveform acquisition unit including an artificial neural network learned in advance and configured to generate an electrocardiogram signal (“ECG signal”) corresponding to the applied SCG signal according to a learned method.

A method for correcting a geometric distortion in a diagnostic image, said method comprising the steps of: receiving a segmented volumetric image and a surface scan image, corresponding to a maxillofacial anatomy of a patient; aligning the surface scan mesh to a volumetric image; and applying a transformation to the surface scan image mesh in which the geometry of a HV-LD is altered yielding the surface scan image mesh with tooth crowns closely corresponding to the volumetric image teeth image resulting in a correction for the geometric distortion.

Systems and methods are disclosed for identifying anatomically relevant blood flow characteristics in a patient. One method includes: receiving, in an electronic storage medium, a patient-specific representation of at least a portion of vasculature of the patient having a lesion at one or more points; receiving values for one or more metrics of interest associated with one or more locations in the vasculature of the patient; receiving one or more observed lumen measurements of the vasculature of the patient; determining the location of a diseased region in the vasculature of the patient using the received values for the one or more metrics of interest, wherein the determination of the location includes predicting or receiving one or more healthy lumen measurements of the vasculature of the patient; determining the extent of the diseased region; and generating a visualization of at least the diseased region.