A first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

In a method for determining a fat-reduced MR image, a first MR image is provided having, apart from the other tissue constituents, MR signals from only one of the two fat constituents, the first MR image is applied to a trained ANN, which was trained by first MR training data as the input data, the training data including, apart from the other tissue constituents, MR signals from only the one of the two fat constituents, and using second MR training data as a base knowledge, the second MR training data including, apart from the other tissue constituents, no MR signals from the two fat constituents; and an MR output image is determined from the trained ANN, to which the first MR image was applied, as a fat-reduced MR image, wherein the fat-reduced MR image includes, apart from the other tissue constituents, no MR signals from the two fat constituents.

Systems and methods for providing algorithmic equipment and/or accessory recommendations are disclosed herein. In one embodiment, a method providing an equipment or accessory recommendation to an athlete includes: monitoring a first amplitude of a first muscle of the athlete by a first wearable muscle response sensor carried by the athlete; monitoring a second amplitude of a second muscle of the athlete by a second wearable muscle response sensor carried by the athlete; determining a difference between the first amplitude and the second amplitude; comparing the difference to a predetermined amplitude threshold; and based on the comparing, providing an equipment or accessory recommendation to the athlete.

Systems and methods for remotely monitoring hospital patients are provided. Medical devices may capture biometric data associated with a patient positioned in an ICU environment, and a remote display device positioned external to the ICU environment may display an indication of the health of the patient based on the captured biometric data. The indication of the health of the patient may include a graph mapping normalized health statuses associated with the patient over time. Each normalized health status may be based on normalizing a particular type of biometric data based on a medical protocol, so each data point of the graph includes an indication of a normalized health status associated with the patient at a given time. The indication of the health of the patient may include an animated three-dimensional that is dynamically updated to reflect biometric data associated with the patient captured at a time selected by a user.

An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Improved assessment of brain injuries, and improved brain injury management, is achieved using a combination of impact-related data derived from instrumented mouthguard devices, human function performance testing, and biomarkers derived from biological fluid (such as saliva and/or blood). The human function performance testing may include brain function performance testing, and/or other forms of human function performance testing. This involves combining a data-driven understanding of a head impact event (based on data collected via an instrumented mouthguard device) with a data-driven understanding of human function performance following that head impact event. The biomarkers may include salivary mRNA and/or ncRNA, and/or blood proteins (for example via a FDA-approved Brain Trauma Indicator test).

A method for monitoring a patient in a bed using a camera. The method includes identifying a boundary of the bed using data from the camera, identifying parts of the patient using data from the camera, and determining an orientation of the patient using the parts identified for the patient. The method further includes monitoring movement of the patient using the parts identified for the patient and computing a departure score indicating the likelihood of the patient departing the bed based on the orientation of the patient and the movement of the patient. The method further includes comparing the departure score to a predetermined threshold and generating a notification when the departure score exceeds the predetermined threshold.

A method and system are provided for estimating a physiological parameter using a parameter model determined by a deep neural network. An example method includes training a deep neural network with indirect and direct physiological parameters from a user database. The medical parameters include a respiratory rate, oxygen saturation, temperature, blood pressure, and pulse rate. The method includes determining if a new user belongs in a group. If the parameter model estimated physiological parameter using the closest group to the new user and associated calibration, then the method quantizes the parameter inputs to determine which physiological parameter a new user is most sensitive and to determine a new group and calibration coefficients or curves for the new user.