Each accessory device in a set of accessory devices may establish a respective communication link between the accessory device and an ear-wearable device. A particular accessory device in the set of accessory devices may receive data via the communication link between the particular accessory device and the ear-wearable device. The data comprise information generated based on sensor signals from sensors that monitor a user of the ear-wearable device. The accessory devices perform a health monitoring activity based on the data.

The present inventive concept relates to a non-face-to-face health status measurement system and method through a camera-based vital sign data extraction and an electronic questionnaire, providing a system and a method for monitoring various infectious diseases and enabling rapid response to the occurrence of diseases and infectious diseases by measuring a health status of a user in non-face-to-face through a vital sign data including heart rate, respiration rate, oxygen saturation, etc. extracted by using color information of a face image taken with a camera and results of an electronic questionnaire performed online.

The present disclosure generally relates to user interfaces for health applications. In some embodiments, exemplary user interfaces for managing health and safety features on an electronic device are described. In some embodiments, exemplary user interfaces for managing the setup of a health feature on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described. In some embodiments, exemplary user interfaces for managing a biometric measurement taken using an electronic device are described. In some embodiments, exemplary user interfaces for providing results for captured health information on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described.

A system and method for extracting a cardiac signal from a spinal signal include measuring a spinal signal at one or more electrodes that are connected to a neurostimulator and implanted within a patient's spinal canal and processing the spinal signal to extract the cardiac signal, which includes features that are representative of the patient's cardiac activity. Processing the spinal signal to extract the cardiac signal can include filtering the spinal signal, or use of model reduction schemes such as independent component analysis. The extracted cardiac signal can include a number of features that correspond to an electrocardiogram and can be used to determine the patient's heart rate and/or to detect a cardiac anomaly. Cardiac features that are determined from the cardiac signal can additionally be used to adjust parameters of the stimulation that is provided by the neurostimulator.

A bio-signal measuring apparatus includes a sensing apparatus configured to sense electrocardiogram data representing an electrical change according to a pulse of an object and sense heart sound data according to the pulse and a processing apparatus configured to store the electrocardiogram data in a memory. The processing apparatus is further configured to analyze the electrocardiogram data to determine whether or not an abnormal signal is generated in the electrocardiogram data, when the abnormal signal is detected to be generated in the electrocardiogram data, generate a storage control signal for heart sound data associated with the abnormal signal in an abnormal signal section including the abnormal signal, and store the associated heart sound data in the abnormal signal section of the memory in response to the storage control signal.

Devices and methods for determining item-specific information for single or multiple items on one or multiple substrates are described. The method includes generating multiple sensor multiple dimensions array (MSMDA) data from multiple sensors, where each of the multiple sensors capture sensor data for one or more items in relation to a substrate. For each item, the method includes determining relationships between the multiple sensors based on characteristics of the MSMDA data, determining a location of the item on the substrate based on at least the determined relationships between the multiple sensors, determining an angular orientation of the item on the substrate based on at least the determined relationships between the multiple sensors, and determining a body position of the subject on the substrate based at least the determined relationships between the multiple sensors, the location of the subject, and the angular orientation of the item.

An automatic diagnostic apparatus and corresponding method is disclosed for recognizing heart sounds of interest, i.e., murmurs, detected in streaming audio data picked up by a stethoscope. Sensors included in the device capture audio data in real time during an auscultation exam performed by a physician. A feature vector that models the stream of audio data is created and supplied to a deep neural network stored on the diagnostic device. The deep neural network generates a probability for each of the heart sounds of interest. When the probability of detection exceeds a pre-established threshold value the device alerts the physician through visual and/or audio cues, enhancing the physician's diagnostic capability during routine examination.

A capacitive sensing system is adapted for noninvasive measurement of blood pulse (hear rate). A capacitive sensor is located near a skin pulse point exhibiting pulse displacement of skin tissue from vascular pulsation (for example, the temple area of the head), and includes a sensor electrode disposed over and spaced from the skin pulse point, such that the distance between sensor electrode and the skin pulse point cycles between a proximal and a distal displacement distance based on vascular pulsation. A capacitance-to-digital conversion (CDC) unit includes excitation circuitry providing sensor excitation to generate a sensor E-field between the sensor electrode and the skin pulse point based on sensor self-capacitance, and capacitance acquisition/conversion circuitry that acquires capacitance measurements for proximal and distal self-capacitance (for example, by multi-phase capacitive charge transfer using a switched capacitor arrangement), and converts these capacitance measurements into sensor data representative of vascular pulsation.

A charging station for providing power to a physiological monitoring device can include a charging bay and a tray. The charging bay can include a charging port configured to receive power from a power source. The tray can be positioned within and movably mounted relative to the charging bay. The tray can be further configured to secure the physiological monitoring device and move between a first position and a second position. In the first position, the tray can be spaced away from the charging port, and, in the second position, the tray can be positioned proximate the charging port, thereby allowing the physiological monitoring device to electrically connect to the charging port.

Methods for training a model for use in monitoring a health parameter in a person are disclosed. In an embodiment, a method involves monitoring a blood pressure of a person using a control blood pressure monitoring system, receiving control data that corresponds to the monitoring using the control blood pressure monitoring system, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from blood in a blood vessel of the person, wherein the stepped frequency scanning data is collected through multiple receive antennas over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a blood pressure of a person.