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 method of dynamically monitoring a health goal using a wearable device. The method may include receiving, by a processor, user input associated with the wearable device worn by an individual. The method may include obtaining, by the processor, health parameters associated with the individual from the wearable device and user feedback from the individual based on a physical condition of the individual associated with the health plan. Additionally, the method may include evaluating whether the health goal will be successfully reached by the individual following the health plan. Responsive to determining that the health goal will not be successfully reached, the health plan may be dynamically adjusted and provided to the individual to reach the health goal.

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: A display method and apparatus, a wearable device, and a computer-readable storage medium. The method is applied to the wearable device. The wearable device comprises an inertial sensor and at least two screen display areas; when a user wears the wearable device, the screen display areas are not located on the same plane at the same time. The method comprises: obtaining a target action of a user by means of measurement data collected by the inertial sensor; determining the screen display area corresponding to a sight range of the user according to the target action of the user; and lighting the screen display area to display the current content.

Abstract: A magnet resonance system including a magnet, gradient coils, and radiofrequency coils. The gradient coils have one or more first electrically conductive loops that provide spatial encoding as to a subject of the system. The radiofrequency coils have one or more second electrically conductive loops that transmit a radiofrequency field to excite nuclear spins and/or receive an MRI signal. All or a portion of the one or more first electrically conductive loops and the one or more second electrically conductive loops are shared. The present teachings provide a device that integrates part of one or more gradient coils into part of one or more RF coils in a magnetic resonance system.

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: Provided a method, an apparatus, a wearable device and a storage medium for audio playback, and relates to the fields of machine learning, deep learning, intelligent wearable, etc. The method includes: measured vital sign data of a user within a first time range is obtained; a vital sign prediction is performed based on the measured vital sign data within the first time range, to obtain predicted vital sign data of the user within a second time range after the first time range; one or more target audio parameters are determined based on the predicted vital sign data and user attribute information of the user; and matched target audio data is obtained based on the one or more target audio parameters.

Abstract: Provided are a method, apparatus, computer device, and storage medium for music recommendation based on a wearable device, and relates to the field of computer technologies. The method for music recommendation includes: obtaining one or more physiological parameters of a target user collected by the wearable device; inputting the one or more physiological parameters of the target user into a trained relaxation state assessment model to determine a current relaxation state of the target user; and determining at least one piece of target recommendation music based on at least one of the current relaxation state of the target user or relaxation parameters corresponding to multiple pieces of music to be recommended, wherein the at least one piece of target recommendation music is configured to be played for the target user.

Abstract: A method of acute stressors detection includes obtaining, by a processor, heart rate variability (HRV) data determined based on an HRV-related metric associated with an individual; determining, based on the HRV data, whether an HRV event indicative of a maladaptation risk of the individual has occurred; and responsive to determining that the HRV event indicative of the maladaptation risk of the individual has occurred, using a causal inference engine to determine at least one acute stressor as a probable cause of the HRV event based on inputs comprising the HRV event and lifestyle data associated with the individual, wherein the lifestyle data comprises at least one lifestyle event and environmental contexts associated with the at least one lifestyle event performed by the individual.

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: Provided are sleep quality assessment methods, apparatuses, electronic devices, and storage medium, and relates to the field of artificial intelligence and deep learning technologies, and in particular to sleep quality assessment methods, apparatuses, electronic devices, and storage medium. The method includes: determining sleep data of a subject; obtaining sleep feature data based on a reference core sleep period of the subject and the sleep data; and evaluating sleep quality of the subject based on the sleep feature data. The method assesses the sleep quality of the subject based on the sleep feature data extracted based on the reference core sleep period of the subject and the sleep data of the subject, takes into account individual factors of the subject in extracting the sleep feature data, thus providing a more accurate assessment of the sleep quality of the subject.

Abstract: Methods, apparatuses, electronic devices for MRI scanning are provided, in which in each scanning plane segment, original k-space data of a current scanning plane is obtained, and a first plane image of the current scanning plane is generated based on the original k-space data, the type of the current scanning plane is determined, and the second plane image of the current scanning plane is generated based on the first plane image of the current scanning plane with a prediction mode corresponding to the type of the current scanning plane.

Abstract: A radio-frequency apparatus for magnetic resonance imaging (MRI) and/or magnetic resonance spectroscopy (MRS) transmission comprising: at least one first coil resonator and at least one second coil resonator including a plurality of conductive elements, one or more of capacitive elements in each of the at least one first coil resonator and the at least one second coil resonator; the first and second coil resonators are located in a same layer of the radio-frequency apparatus with a same mode, are parallel to an axis of the subject being imaged and are electromagnetically isolated relative to each other; major components of radio-frequency fields generated by the first and second coil resonators extend in a direction that is orthogonal and perpendicular to a main magnetic field; and a combination of the first and second coil resonators are a radio-frequency apparatus to excite nuclear spins for the MRI and the MRS.

Abstract: Provided are a display method and apparatus, a smart wearable device, and a computer-readable storage medium, the display method being used on the smart wearable device, and the smart wearable device including at least two screen display regions and at least two corresponding audio collection units; when a user is wearing the smart wearable device, the screen display regions are not simultaneously in the same plane. The display method includes according to voice signals collected by all of the audio collection units, determining an audio collection unit nearest a sound source; turning on a screen display region corresponding to the audio collection unit nearest the sound source to display current content.

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: The present disclosure relates to a method and a system for minimizing electromagnetic interference (EMI) of magnetic resonance imaging (MRI) systems in complex electromagnetic environments. The method and system described herein provide superior MRI image quality and may reduce costs by eliminating the requirement of expensive shielding in MRI systems/hybrid MRI systems.

Abstract: The present disclosure relates to a method and a system for minimizing electromagnetic interference (EMI) of magnetic resonance imaging (MRI) systems in complex electromagnetic environments. The method and system described herein provide superior MRI image quality and may reduce costs by eliminating the requirement of expensive shielding in MRI systems/hybrid MRI systems.

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.