Disclosure is a hearing threshold and/or hearing state detection system and method. The system comprises: an acquisition and transmission system configured to transmit stimulation signals and acquire an ear canal signal; and a hearing threshold analysis and prediction system including a hearing threshold detection module, a routine testing module and/or a hearing state screening module, wherein the hearing threshold detection module determines hearing thresholds at different stimulation frequencies through a pre-trained network model; the routine testing module adaptively selects a range of test intensities through the acquisition and transmission system, and predicts hearing thresholds related to different stimulation frequencies through a pre-trained network model; and the screening module is configured to perform hearing state screening through the acquisition and transmission system and a pre-trained network model. A detection result thereof is not only accurate, but also is applicable to various demand scenarios.

Technology is disclosed for detecting scratch events and predicting flares of pruritus, utilizing motion data sensed from a wearable sensor. Detecting scratch may be done with a two-tier approach by first detecting a hand motion from motion sensed data and then classifying that hand motion as a scratch event using one or more computerized classification models. Embodiments may focus on detecting nighttime scratch by utilizing motion sensed data captured during a user's detected sleep opportunity. Additionally, historical scratch event data may be used to predict a user's itch and flare risk for a future time interval. Decision support tools in the form of computer applications or services may utilize the detected scratch events or predicted itch or flare risk to initiate an action for reducing current itch and/or mitigating future risk, including initiating a treatment protocol that includes therapeutic agent.

Described herein are computer-implemented methods for analyzing an input image of a mouth region from a user to provide information regarding a disease or condition of the mouth region, a computing device configured to receive the input images from a user; and a trained machine learning system. In some embodiments, the computing device is configured to transmit an oral health score to the user.

A jaundice diagnosis and treatment system includes a control circuit, a bilirubin measurement assembly, a risk evaluation circuit and a display assembly, wherein the bilirubin measurement assembly is communicatively connected to the control circuit, and is used for measuring a bilirubin concentration of a newborn baby and transmitting the measured bilirubin concentration to the control circuit, so that the control circuit generates measurement data according to the measured bilirubin concentration; the risk evaluation circuit is communicatively connected to the control circuit, and is used for acquiring the measurement data from the control circuit and evaluating a pathologic jaundice risk level according to the measurement data; and the display assembly is connected to the control circuit, and is used for displaying the measurement data and/or the pathologic jaundice risk level under the control of the control circuit.

A method and device for predicting Alzheimer's disease based on voice characteristics are provided. The device for predicting Alzheimer's disease according to an embodiment includes: a voice input unit configured to generate a voice sample by recording a voice of a subject; a data input unit configured to receive demographic information of the subject; a voice characteristic extraction unit configured to extract voice characteristics from the generated voice sample; and a prediction model that is pre-trained to predict presence or absence of Alzheimer's disease in the subject, based on the voice characteristics and the demographic information.

A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.

There is a need for a technique to determine a presence or absence of morbidity of a lifestyle-related disease and a possibility of future morbidity (risk of morbidity) in a non-invasive manner for a subject. The present disclosure provides a blood abnormality prediction device including, a prediction unit configured to predict a presence or absence of a blood abnormality in a subject on the basis of the information of the image that captures a crown portion of a capillary, wherein the prediction unit is configured to measure one or more selected from the group consisting of an entire width, an apex width, a loop diameter, a venous limb width, and an arterial limb width of the crown portion of the capillary on the basis of the information of the image, to predict the presence or absence of the blood abnormality in the subject from a result of the measurement.

A motion monitoring method (500) is provided, which includes: obtaining a movement signal of a user during motion, wherein the movement signal includes at least an electromyographic signal or an attitude signal (510); and monitoring a movement of the user during motion based at least on feature information corresponding to the electromyographic signal or the feature information corresponding to the attitude signal (520).

Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.