Systems and methods for assisting user with hearing by amplifying sound using an amplifier with gain and amplitude controls for a plurality of frequencies; and applying a learning machine to identify an aural environment and adjust the amplifiers for optimum hearing.

An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

An article of footwear includes an upper and a sole structure with a sole member. The sole member can include a set of apertures that is formed along various surfaces of the sole member. The sole member can be manufactured using a customized cushioning sole system, forming generally circular patterns throughout the sole member. A user's foot morphology and/or preferences may be used to design the sole member.

Systems and methods for generating a 3D model is provided. The methods may include receiving target information relating to the subject acquired by an information acquisition component and generate the 3D model of the subject based on the target information. The target information may include a plurality of images of the subject. The subject may be supported by a scanning table, and the information acquisition component may include a plurality of cameras mounted on a gantry of a medical imaging device. The plurality of cameras may be configured to acquire the plurality of images of the subject from multiple perspectives, and at least one of the plurality of cameras may be configured to acquire an image of a body side of the subject.

A computing system configured to perform operations that identify user movement(s) as upper-body exercise(s). The operations can include obtaining sensor data generated by one or more sensors of a footwear device worn by a user, the one or more sensors positioned in one or more positions of the footwear device, the sensor data associated with one or more movements performed by the user. The sensor data can be input into a machine-learned exercise identification model. Exercise identification data can be received as an output of the machine-learned exercise identification model. The exercise identification data can identify the one or more movements performed by the user as one or more upper-body exercises performed by the user, the exercise identification data comprising one or more exercise characteristics associated with each upper-body exercise of the one or more upper-body exercises performed by the user.

An imaging device that images a disease site as a subject includes a camera body, a light unit that is provided in the camera body and includes a first light source and a second light source that have different characteristics, and a filter unit that includes at least one independent filter capable of being positioned on and retracted from an optical axis of the camera body. The imaging device performs continuous imaging by imaging in a state in which the subject is illuminated with light from the first light source and, via a first mode, the filter is positioned on or retracted from the optical axis and, thereafter, imaging in a state in which the subject is illuminated with light from the second light source and, via a second mode that differs from the first mode, the filter is positioned on or retracted from the optical axis.

A biometric sensor includes a body surface sensor and an e-field signal transmitter. The body surface sensor create a drive-sense signal at a first frequency based on one or more sensing parameters. When operably coupled to a body via one or more electrodes, the body surface sensor provides the drive-sense signal to the body and detects an effect on the drive-sense signal based on electrical characteristics of the body. The body surface sensor generate a data signal based on the detected effect, wherein the data signal represents the body’s electrical characteristics. The e-field signal transmitter generates an outbound signal reference at a second frequency based on the data signal and one or more transmit parameters. The e-field transmitter drives the outbound reference signal to the body, wherein the outbound reference signal is transmitted within at least a portion of the body as an outbound e-field signal at the second frequency.

A determination system includes a body surface change information acquirer, a motion detector, and a physiological state determiner. The body surface change information acquirer acquires body surface change information indicating a chronological change in body surface data obtained from a part of a body surface of a subject. The motion detector detects a motion of the subject. The physiological state determiner determines a physiological state of mind or body of the subject based on the body surface data acquired upon detecting the motion of the subject.

The present invention relates to an automatic cervical cancer diagnosis system for performing machine learning by classifying cervical data required for automatic diagnosis of cervical cancer according to accurate criteria and automatically diagnosing cervical cancer based on the machine learning, the automatic cervical cancer diagnosis system including: a learning data generator configured to classify unclassified photographed image data for a cervix transmitted from an external device or a storage according a combination of multi-level classification criteria to generate learning data for each new classification criterion in a learning mode; a photographed image pre-processer configured to pre-process photographed cervix images; a cervical cancer diagnoser including a machine learning model for cervical cancer that learns a characteristic of the learning data generated for each classification criterion in the learning mode, wherein the machine learning model generates diagnosis information about whether cervical cancer has occurred with respect to the pre-processed photographed cervix image; a screen display controller configured to display and output a user interface screen configured to display the diagnosis information and inputting evaluation information according to a reading specialist; a retraining data generator configured to extract information required for retraining from the evaluation information input through the user interface screen and request retraining the machine learning model; and a diagnosis and evaluation information storage configured to store the diagnosis information about whether cervical cancer has occurred and the evaluation information input through the user interface screen.

An upper-arm computer pointing apparatus, comprising: at least one orientation measurer, deployable on at least one area of an upper arm of a user, configured to measure orientation of the upper arm, at least one pressure meter, deployable on at least one area of the upper arm, configured to measure pressure applied by muscle of the upper arm, a computer processor, associated with the orientation measurer and pressure meter, configured to derive control data from the measured orientation and pressure, and a data transmitter, associated with the computer processor, configured to transmit the control data to a computing device.