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.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

A method for site assessments of a catheter insertion site and/or dressing includes: scanning the catheter insertion site and/or dressing with an image capture device and/or sensor; selecting a patient baseline site location and skin tone; recording a baseline condition using a computing device; determining a site assessment rate using a computing device; prompting a clinician to make a site assessment of the catheter insertion site and/or dressing using a computing device; and recording site assessment information in an electronic medical record using a computing device.

Embodiments of the present disclosure relate to method and system for diagnostic analyzing. Some embodiments of the present disclosure provide a diagnostic analyzing system. The diagnostic analyzing system comprises one or more analyzer instruments and a monitoring system, e.g. a quality control monitoring system. The one or more analyzer instruments designed for providing an analytical testing result, which is to be validated by the monitoring system using a validation algorithm. Moreover, the monitoring system may re-train the validation algorithm when a difference level between a live data set and a first training data set is greater than a threshold. Through the solution, it is possible to improve the accuracy of the validation algorithm.

A processing system obtain signals that are generated by or generated based on sensors that are included in one or more hearing instruments. Additionally, the processing system determine, based on the signals, whether a posture of a user of the hearing instruments is a target posture. The processing system generate information based on the posture of the user.

In an embodiment, an electrogram (EGM) processing system provides, for display by a head-mounted display (HMD) worn by a user, a holographic rendering of intracardiac geometry. The HMD also displays an electrogram waveform. The EGM processing system determines a gaze direction of the user by processing sensor data from the HMD. The HMD displays a marker overlaid on the electrogram waveform at a location based on an intersection point between the gaze direction and the electrogram waveform. The EGM processing system determines a measurement of the electrogram waveform using the location of the marker. The HMD displays the measurement of the electrogram waveform.

Disclosed is a method of identifying dementia by at least one processor of a device. The method includes performing a first task that causes a first object to be displayed on a first region of a screen displayed on a user terminal; and when a preset condition is satisfied, performing a second task that causes at least one object, which induces the user's gaze, to be displayed instead of the first object on the screen of the user terminal.

A mixed reality (MR) training system includes an identification algorithm (ML1) that identifies incidents of concern (IOCs) based on an incident report data set and related contextual data. Each IOC occurs in the data set with a frequency at least equal to a pre-determined threshold or the resulting consequence is at least equal to a different pre-determined threshold. The system also includes a prediction algorithm (ML2) configured to identify predicted changes in the frequency or contextual data of incidents, an experience generation algorithm (ML3) configured to generate an MR training experience based on IOCs identified by ML1 and the predictions of ML2. A fourth algorithm (ML4) tailors and optimizes MR generated training experiences based, in part, on (i) changes in the incident report data or contextual data or (ii) performance data or biometric response data received during or after a user's interaction with the MR training experience.

Biometric enabled virtual reality (VR) systems and methods are disclosed for detecting user intention(s) and modulating virtual avatar control based on the user intention(s) for creation of virtual avatar(s) or object(s) in holographic space, two-dimensional (2D) virtual space, or three-dimensional (3D) virtual space. A virtual representation of an intended motion of a user corresponding to an intention of muscle activation of the user is determined based on analysis of a biometric signal data of the user as collected by a biometric detection device. The virtual representation of the intended motion is used to modulate virtual avatar control or output to create at least one of a virtual avatar representing aspect(s) of the user or an object manipulated by the user in a holographic space, virtual 2D space, or virtual 3D space. The avatar or the object is created based on: (1) the biometric signal data of a user, or (2) user-specific specifications as provided by the user.