A hand-held sized ocular and neurological screening device, system and method, the screening device comprising an eyepiece and a hand-held housing, the housing comprising a tubular stimulus chamber defining a light stimulus channel, wherein an illumination source is configured to provide light stimulus towards an opening through the light stimulus channel and an operational chamber comprising an infrared camera positioned outside the stimulus channel and inclined towards the opening, the infrared camera is configured to capture images of the pupils and eye movements through the opening without interfering with the light stimulus and a controller configured to receive the captured images from the infrared camera. The hand-held sized device can include a clip-on fixture for fixing the device onto a table, a desktop, or any portable ophthalmic apparatus.

Methods and systems are provided for detecting a mental health condition. Structured and unstructured information is analyzed using natural language processing to extract information including clinical data values and medical concepts pertaining to a user. Reference medical information is evaluated using natural language processing to correlate medical data with mental health conditions. A classification for a mental health condition of the user is determined using a machine learning model and based on the extracted information and correlations, wherein the extracted information includes blood analysis for the user. The user is assigned to a segment of users based on the extracted information. A treatment for the mental health condition of the user is indicated based on the classification and the assigned segment of users.

The invention relates to methods and systems for determining the intention of a subject to perform a voluntary action based on the analysis of the subject's respiratory phases and neuroelectrical signals.

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.

A patient care method through artificial intelligence-based monitoring in accordance with one example of the present disclosure comprises steps of: obtaining image information relating a user by a first collecting portion, obtaining speech information relating to the user by a second collecting portion and obtaining biometrics information relating to the user by a third collecting portion (Step 1); representing at least a part of a plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion, through a display portion of a user table (Step 2); determining health condition of the user, based on a part of the plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion by a server (Step 3); controlling the display portion of the user table to represent a first information automatically generated based on the determined heath condition by the server (Step 4), wherein the first information is converted in real time based on user's feedback on the first information and represents a change in the determined health condition on the display portion.

Systems and methods are provided to combine multiple stimulation modalities to significantly increase the effectiveness of non-invasive stimulation. Multiple sensor and stimulation devices and modalities can be combined into a single, compact unit that minimizes the need for additional sensors or stimulation devices. The system features several subunits, referred to as sensory and stimulation devices (SSD), that are integrated into a headphone setup. The system is controlled by a centralized controller that communicates with all of the SSDs and with an external computer system that delivers learning material synchronized with the delivery of stimulations and the collection of user responses based on physiological signals.

The invention relates to an interactive system (1) with a user (U) occupying a motor vehicle (100) comprising an interface (3) and an operating system for executing an application. According to the invention, the interactive system (1) comprises a measuring device (5) comprising at least one sensor that is configured to acquire at least one physiological parameter of the user (U), and an embedded processing unit (7). The processing unit (7) comprises an interpretation module that is configured to receive the physiological parameter and to define, on the basis of the physiological parameter, a datum representative of the physiological or emotional state of the user (U), and an analysis module that is configured to compare data representative of the physiological or emotional state of the user (U) before and during the execution of an application for managing a remediation exercise, and to adjust at least one parameter of the remediation exercise according to the results of the comparison. The invention also relates to an interaction method implemented at least partly by such an interactive system.

Systems and methods for detecting onset, presence, and progression of particular states, including intoxication, include observing eye movements of a subject and correlating the observed movements to known baseline neurophysiological indicators of intoxication. A detection system may record eye movement data from a user, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not intoxication or impairment is present. The detection system may alert the user to take corrective action if onset or presence of a dangerous condition is detected. The eye movements detected include saccadic and intersaccadic parameters such as intersaccadic drift velocity. Measurements may be collected in situ with a field testing device. An interactive application may be provided on a user device to provoke the desired eye movements during recording.

Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

A system detects a user's mood and in response establishes computer settings including computer game settings, recommends social network interactions, advises other users, alters task scheduling, and in general enhances collective group mood, collective productivity, social interaction, and engagement.