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.

A method comprises: applying a first trained function to first input data to generate first output data, the first output data including first key elements; receiving second input data, the second input data being an X-ray image of an examination region acquired using a first collimation region; applying a second trained function to the second input data to generate second output data, the second output data including second key elements; receiving third input data in response to an incomplete set of second key elements, the third input data including the second key elements and an X-ray image of the examination region acquired using the first collimation region; applying a third trained function to the third input data to generate third output data, the third output data including an estimated third key element to complete the set of second key elements; and providing a complete set of second key elements.

Methods and systems implement a variety of sensors, including in embodiments various combinations of EEG sensors, biochemical sensors, photoplethysmography (PPG) sensors, microphones, and accelerometers, to detect, predict, and/or classify various physiological events and/or conditions related to epilepsy, sleep apnea, and/or vestibular disorders. The events can include neuroelectrical events, cardiac events, and/or pulmonary events, among others. In some cases, the method and systems implement trained artificial intelligence (AI) models to detect, classify, and/or predict. The methods and systems are also capable of optimizing a treatment window, suggesting treatments that may improve the overall well-being of the patient (including improving pre- or post-event symptoms and effects), and/or interacting with various care providers.

The present technology provides a living body information measurement apparatus and a living body information measuring method that make it possible to discriminate a state of a living body with high accuracy. The present technology provides a living body information measurement apparatus including: a sensor device that applies light to a living body and individually detects light scattered by a plurality of parts in the living body; and a processor that discriminates a state of the living body on the basis of outputs of the sensor device for the respective parts. According to the present technology, it is possible to provide a living body information measurement apparatus and a living body information measuring method that make it possible to discriminate the state of the living body with high accuracy.

Disclosed is multi-modal few-shot learning device for user identification using a walking pattern based on deep learning ensemble. The device includes: a walking data collector configured to collect walking data of a user from a smart insole including any one or more of a pressure sensor, an acceleration sensor, and a gyro sensor; a preprocessor configured to convert a series of time series walking data obtained from each of the sensors included in the smart insole into a unit format data set; and an ensemble learner configured to apply an ensemble learning model that provides one final prediction by training CNN series learning and RNN series learning respectively and independently based on the unit-format data set generated by the preprocessor.

A method and system for detecting spinal stenosis is provided. The method may receive image data corresponding to a spine region of a patient. The method may also identify a spinal cord in the image data. The method may determine at least one compression of the spinal cord and may mark an anatomical element proximate to a location of the determined at least one compression to yield at least one marking. The method may generate a decompression plan based on the at least one marking.

A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

A respiratory status examination apparatus and method, and a sleep disorder control device and method are proposed. The apparatus may include at least one image capturing unit that is movably arranged to adjust a distance with respect to a subject and configured to obtain a thermal image by photographing the subject. The apparatus may also include a motion sensor unit configured to detect a motion of the subject to generate motion information, and a temperature information extracting unit configured to specify at least one examination region from the thermal image obtained by the image capturing unit and extract temperature information from the examination region. The apparatus may further include a respiratory status examining unit configured to determine a respiratory status of the subject based on the temperature information extracted by the temperature information extracting unit and the motion information generated by the motion sensor unit.

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.