System, method, and media for providing navigation of an affective vehicle environment to an occupant (such as a driver) of a vehicle. Sensors in and on the vehicles in the affective environment may detect characteristics of the occupants of the vehicle and stress levels may be assigned to the occupants. The navigation may be based at least in part on the location and destination of the vehicles in the affective environment and the stress levels of the occupants of the vehicles.

System, method, and media for providing navigation of an affective vehicle environment to an occupant (such as a driver) of a vehicle. Sensors in and on the vehicles in the affective environment may detect characteristics of the occupants of the vehicle and stress levels may be assigned to the occupants. The navigation may be based at least in part on the location and destination of the vehicles in the affective environment and the stress levels of the occupants of the vehicles.

Described here are systems and methods for monitoring airflow changes in a patient's airway during a medical procedure or as a general patient monitoring tool. Doppler ultrasound signals are acquired from the tracheal wall of the patient and parameters from those Doppler ultrasound signals are compared to baseline parameters. When a threshold change is detected, an alarm can be provided to a user to indicate respiratory compromise, which can include early airway compromise or airway obstruction.

Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.

A detected sound signal may comprise speech or non-verbal sounds, and many non-verbal sounds contain health information. If the speech, or a non-verbal sound containing health information, was produced by an enrolled user, data relating to the sound can be stored in a storage element. A system also comprises a data modification block, for obfuscating received data to provide an obfuscated version of the stored data. The system then has a first access mechanism, for controlling access to the stored data such that only an authorised user can obtain access to said stored data, and a second access mechanism, for controlling access to said stored data such the second access mechanism only provides access to the obfuscated version of the stored data.

Example methods, apparatus, and articles of manufacture for monitoring use by a user of a portable research device in accordance with at least one predetermined use criterion are disclosed. The disclosed examples include passively gathering data for assessing an identity of a user of the portable research device, processing the passively gathered data to produce assessment data indicating a possibility that the user is not a predetermined correct user of the portable research device, based on the assessment data, displaying a message to the user requesting a response from which the user's identity may be determined, and processing a response to the message to produce data indicating whether the user is the predetermined correct user.

A method is disclosed (100) for obtaining respiratory related sounds (160), RRSs, originating from a target patient, the method comprising the steps of obtaining an input audio recording (110, 111) of a sleeping environment of the target patient, obtaining a respiratory trace (150) of the target patient's respiration, identifying (120) RRSs (130) in the input audio recording, and selecting (140), based on the respiratory trace, from the RRSs, the RRSs (160) originating from the target patient. The selecting further comprises: determining a first and/or second subset of the RRSs having a respective high and/or low probability of originating from the target patient, training a classifier based on the first and/or a second subset to select RRSs originating from the target patient, and selecting the RRSs originating from the target patient (160) by the trained classifier.

A system for clinical decision support for a caregiver during a clinical encounter involving respiratory distress includes a defibrillator-patient monitor including sensors configured to gather patient physiological data, a user interface device, a memory, and a processor communicatively coupled to the defibrillator-patient monitor, the memory, and the user interface device and configured to control the user interface device to display diagnosis and treatment pathways (DTPs) including a respiratory distress DTP, receive a user selection of the respiratory distress DTP, control the user interface device to provide at least a portion of the physiological data in response to the user selection of the respiratory distress DTP, provide a suggested respiratory distress diagnosis at the user interface device based on the physiological data and the user selection of the respiratory distress DTP, and provide a respiratory distress treatment protocol at the user interface device based on the suggested respiratory distress diagnosis.

A system for clinical decision support for a caregiver during a clinical encounter involving respiratory distress includes a defibrillator-patient monitor including sensors configured to gather patient physiological data, a user interface device, a memory, and a processor communicatively coupled to the defibrillator-patient monitor, the memory, and the user interface device and configured to control the user interface device to display diagnosis and treatment pathways (DTPs) including a respiratory distress DTP, receive a user selection of the respiratory distress DTP, control the user interface device to provide at least a portion of the physiological data in response to the user selection of the respiratory distress DTP, provide a suggested respiratory distress diagnosis at the user interface device based on the physiological data and the user selection of the respiratory distress DTP, and provide a respiratory distress treatment protocol at the user interface device based on the suggested respiratory distress diagnosis.

A method determining lung pathology severity from a subject under test includes receiving a training set comprising a plurality of breath flow signals and a plurality of audio signals for a convolutional neural network (CNN). The method includes training a convolutional neural network and creating at least one test graph using a breath flow signal and an audio signal from the subject under test. The method further includes inputting the at least one test graph associated with the subject under test into the CNN and determining an existing pathology and associated severity for the subject under test. Also, the method includes determining a prediction for a future possible condition of the subject and determining the lung pathology severity be computing a distance between the future possible condition of the subject under test and the existing pathology and associated severity.