Abstract: A first physiological metric associated with a person is identified based on sensor data. A first audio track is identified based on the first physiological metric. The first audio track is then output. The first physiological metric includes at least one of a cadence, a heartrate, a micro-movement, or a respiration rate. A second physiological metric obtained during a playback of the first audio track is identified. A second audio track is identified based on the second physiological metric. The second audio track is output after the first audio track.

Abstract: Training data are obtained. Each training datum includes environment characteristics obtained based on sensor data. Respective user settings corresponding to the training data are obtained. At least one respective user setting corresponds to one training datum, and a respective user setting is indicative of a user preference of at least one parameter of a hearing aid device. A machine-learning model for the hearing aid device is trained to output values for the at least one parameter. The hearing aid device is reconfigured based on an output of the machine-learning model. Reconfiguring the hearing aid device includes using current environment characteristics as an input to the machine-learning model to obtain at least one current value for the at least one parameter and configuring the hearing aid device to use at least one current value.

Abstract: A circadian rhythm (CR) oscillator is obtained for a person based at least in part on a bathyphase time. A fatigue value is calculated for the person. Calculating the fatigue value includes, in response to determining that the person is in sleep, subtracting a sleep recovery unit from the fatigue value in a time unit; and accumulating, in a first time range of the sleep, a total sleep inertia by an inertia in sleep in the time unit. The sleep recovery unit is calculated based on the CR oscillator and a recovery debt In response to determining that the person is performing an activity and determining that the fatigue value is above a threshold, the person is notified to recover to decrease the fatigue value below the threshold prior to performing the activity.

Abstract: A system and methods are used to enable hearing device calibration using auditory filters. The system and methods are used to reduce processing time for estimating auditory filters for new hearing device users and can be performed by the user without the aid of an audiologist. The system and methods use a database of notched-noise test results of other users to create a prior distribution of possible auditory filter parameters. The prior distribution is used to minimize the number of notched-noise test performed, thereby reducing the amount of calibration time.

Abstract: A system and methods are used to enable hearing device calibration using auditory filters. The system and methods are used to reduce processing time for estimating auditory filters for new hearing device users and can be performed by the user without the aid of an audiologist. The system and methods use a database of notched-noise test results of other users to create a prior distribution of possible auditory filter parameters. The prior distribution is used to minimize the number of notched-noise test performed, thereby reducing the amount of calibration time.

Abstract: A device including sensors and a processor. The sensors are configured to detect movements of a user. The processor is configured to categorize the movements of the user as a micro-movement or a macro-movement; quantify a number of the micro-movements; quantify a number of the macro-movements; determine based upon the number of micro-movements whether a body part of interest of a user is supported; and provide feedback to the user if the body part of interested is unsupported and continuing to monitor the body part of interest if the user is supported without providing any feedback.

Abstract: A method includes obtaining a baseline blood pressure at an initial time window; estimating a plurality of intermediate blood pressure change estimates, the intermediate blood pressure change estimates correspond to respective time windows that are subsequent to the initial time window; estimating a final blood pressure change estimate between the initial and final time windows; and obtaining the blood pressure by adding the baseline blood pressure to the final blood pressure change estimate. Estimating the final blood pressure includes estimating a first blood pressure change between the initial time window and the final time window; estimating a plurality of second blood pressure changes, each second blood pressure change is between a respective time window of the respective time windows and the final time window; and estimating the final blood pressure change estimate as a combination of the first blood pressure change and the plurality of second blood pressure changes.

Abstract: Apnea-hypopnea detection includes obtaining accelerometer data from an accelerometer configured to measure micro-movements that are due to respiration. Displacement values are obtained from the accelerometer data. Features are obtained using the accelerometer data. An apnea-hypopnea index (AHI) is obtained from a machine learning model that uses the features as inputs. The displacement values correspond to peaks in the accelerometer data.

Abstract: A device including sensors and a processor. The sensors are configured to detect movements of a user. The processor is configured to categorize the movements of the user as a micro-movement or a macro-movement; quantify a number of the micro-movements; quantify a number of the macro-movements; determine based upon the number of micro-movements whether a body part of interest of a user is supported; and provide feedback to the user if the body part of interested is unsupported and continuing to monitor the body part of interest if the user is supported without providing any feedback.

Abstract: A wearable device includes a processor and a lower module. The lower module includes a pressure sensor for detecting a mechanical movement of a skin that covers an artery. The mechanical movement of the skin is due to blood flow through the artery. The processor is configured to receive skin movement information from the movement sensor; calculate a pulse front velocity (PFV), which is a velocity of a blood wave as the blood wave passes under the pressure sensor; estimate a pulse wave velocity (PWV) using the PFV; and estimate the blood pressure using the PWV.

Abstract: A device comprising: (a) an accelerometer or gyroscope that is capable of measuring movement, and (b) a sensor that is configured to monitor and determine a electrical signals or pulse signals of a heart so that the sensor detects a heart rate of a user; wherein the device is configured for placement on a first location of the user to determine a heart rate of the user; wherein the device is configured for placement on or contact with a second location of the user where the sensor measures the user's heart rate and the accelerometer or gyroscope measures a heart rate of a fetus located within the user; and (c) a processor configured to isolate the heart rate of the fetus from the heart rate of the user so that the heart rate of the fetus is displayed on the device.

Abstract: Examples described herein include systems and methods for determining sleep characteristics based on movement data. In one example, a device including at least one accelerometer can be worn by a user during the night. The accelerometer can be configured to detect small motions of the user and provide an output signal. The output signal can be filtered and used to determine whether the user was sleeping during a particular timeframe. The output signal can also be used to determine a sleep characteristic of the sleep, such as light sleep, deep sleep, REM sleep, obstructive apnea, or central apnea.

Abstract: Assessing a blood pressure of a user includes obtaining photoplethysmogram (PPG)-related signals of the user; inputting the PPG-related signals to layers of a deep-learning (DL) model, where the layers exclude an output layer; obtaining, from the layers of the DL model, features related to blood pressure; inputting to a machine-learning (ML) model the obtained features, where the ML model is different from the DL model; and obtaining, as an output of the ML model, the blood pressure of the user.

Abstract: A method for modifying cognitive processes includes receiving respective electroencephalogram (EEG) signals from EEG sensors, where the EEG signals are of a brain of a user. Features are extracted from the respective EEG signals. A cognitive state of the brain of the user is obtained from a first machine learning (ML) model that uses the features as input. Feedback parameters of a feedback signal are obtained from a second model that uses the cognitive state as input. The feedback signal is and provided to the user and using a user device according to the feedback parameters.

Abstract: Described herein are systems and methods for collecting physiological information from a user and determining information including, but not limited to, a user's heart rate, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability. Physiological information is collected from one or more modules, each comprising a sensor array. The sensor array(s) can comprise, among other things, light sources, photo detectors, ECG electrodes/sensors, bio impedance sensors, galvanic skin response sensors, tonometry/contact sensors, accelerometers, pressure sensors, acoustic sensors, and electromagnetic sensors. The information collected from a user can be used to cross-reference a database comprising similar information for a number of subjects as well as verified measurements for each subject including, but not limited to, blood pressure, oxygen levels (SvO2), hydration, respiration rate, and heart rate variability.

Abstract: A portable device for measuring an electrocardiogram (ECG) of a user. The portable device includes a first ECG sensor that is configured to touch a body of the user, and a processor. The processor is configured to receive a first ECG measurement at a first location on the body of the user, where the first ECG sensor is placed at the first location of the body; identify a first region of the heart of the user based on the first location; direct the user to move the portable device to a second location on the body of the user; receive a second ECG measurement at the second location on the body; and identify a second region of the heart of the user based on the second location.

Abstract: A method includes obtaining a baseline blood pressure at an initial time window; estimating a plurality of intermediate blood pressure change estimates, the intermediate blood pressure change estimates correspond to respective time windows that are subsequent to the initial time window; estimating a final blood pressure change estimate between the initial and final time windows; and obtaining the blood pressure by adding the baseline blood pressure to the final blood pressure change estimate. Estimating the final blood pressure includes estimating a first blood pressure change between the initial time window and the final time window; estimating a plurality of second blood pressure changes, each second blood pressure change is between a respective time window of the respective time windows and the final time window; and estimating the final blood pressure change estimate as a combination of the first blood pressure change and the plurality of second blood pressure changes.

Abstract: Measuring, using a wearable device, a blood pressure of a user includes extracting, using sensor data of the wearable device, features related to a pulse wave; determining a pulse transit time (PTT); scaling at least one of the features using the PTT to obtain a scaled feature; using the scaled feature as an input to a machine-learning (ML) model; and obtaining, using an output of the ML model, the blood pressure of the user.

Abstract: A system can include a wearable device that obtains real-time physiological data and activity data from a user and transmits that data to another device. A computing device can receive the data and calculate a first HRV score for the user based on physiological data from first time period and a second HRV score for the user based physiological data from a second time period. The device can present the user with at least one of the first and second HRV scores. In one example, a graphical display is provided on a GUI that includes indicators for each day of the week. In response to a user selecting an indicator for a day of the week, the GUI can display an HRV score for the selected day, among other information.

Abstract: A system can include a wearable device that obtains real-time physiological data and activity data from a user and transmits that data to another device. A computing device can receive HRV and activity data and determine whether the user's autonomic nervous system is in a predominantly sympathetic or parasympathetic state. For example, the determination can include comparing an average variance in a portion of the HRV data with a threshold value. In response to determining that the user's autonomic nervous system is in a sympathetic state, the device can perform an action.