The present disclosure, in one embodiment, is a computer-implemented method for the detection of and intervention in traumatic nightmares. In one embodiment, a user wears a watch wirelessly connected to a phone. The watch may include an accelerometer, gyroscope, and heartrate monitor. The application may monitor these sensors and intervene with haptic feedback if the application detects a traumatic nightmare. In one embodiment, the application may include a monitoring module that collects data from the watch's accelerometer, gyroscope, and heartrate sensors. The application may then estimate and record stress levels based on these sensors. The application may also include an intervention module that responds to high stress levels with haptic feedback that increases in intensity of previous efforts to intervene were unsuccessful.

Methods and architecture for using sensor data to offer content, such as purchase or viewing suggestions, to a viewer, are disclosed. Wearable or other external sensor devices may monitor a viewer while the viewer consumes a first media element. The sensor data may then be used to determine an emotional response of the viewer to the media element or to entities found within that element, such as actors, scenes, brands, or objects. Emotional response data for a user may be stored, and may be used either immediately or at a later time to deliver content or make content or purchase suggestions to the viewer.

Emotional loneliness is referred as the absence of an attachment figure in one’s life and someone to turn to. The existing methods use installation of sensors for tracking the movement, behaviour and activity of the person, but most of the efforts are obtrusive in nature. A non-obtrusive method and system for detection of emotional loneliness of a person have been provided. The disclosure is utilizing multiple varied techniques to understand the emotional loneliness. The multiple techniques comprise room change movement anomalies, living room stay anomalies, correlating the living room stay with the bedroom stay and outdoor movement anomalies. The methodology also ensures reduced variance and false positives, as emotional loneliness is finally determined based on more than two positives of above methods. The detection of person’s movement is done using a featured engineered dataset based on collection of raw time series data collected from a plurality of motion sensors.

In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

A method and a system are provided for taking biomarker measurements of patients who have ADHD. Mathematical analysis (e.g., pattern recognition, machine learning and AI algorithms) of the biomarker measurements is used to create a unique personal prediction model and data set for an individual patient. The unique personal data set is used to diagnose and monitor a particular problem of the individual patient associated with ADHD, or to recommend a treatment for a particular problem of the individual patient associated with ADHD, or to predict an outcome of a treatment for a particular problem of the individual patient associated with ADHD.

A system for alerting service animals to perform specified tasks generally includes an alert device which provides a first and a second haptic alert from an alert device worn by a service animal and a monitoring device worn by a handler. The monitoring device is programmed to monitor a physiologic parameter of the handler and has a first mode programmed to transmit a first actuation signal to the alert device when the physiologic parameter exceeds a first threshold such that the alert device provides the first haptic alert to the service animal and a second mode programmed to transmit a second actuation signal to the alert device when the physiologic parameter exceeds a second threshold such that the alert device provides the haptic alert to the service animal. The auditory alert is correlated to a first task and the second haptic alert is correlated to a second task.

A method of treatment for intellectual and developmental disabilities is disclosed herein, wherein the method includes the steps of diagnosing an intellectual or developmental disability in an individual, matching symptoms or characteristics associated with the individual's intellectual or developmental disability, researching what personality traits, interpersonal skills, or life skills are affected by the symptoms or characteristics, providing the individual with a list of topics related to the personality traits, interpersonal skills, or life skills are affected by the symptoms or characteristics, selecting a topic related to the personality traits, interpersonal skills, or life skills are affected by the symptoms or characteristics, assisting the individual in preparing a presentation on the selected topic, preparing the presentation, wherein the presentation is between ten minutes and ninety minutes in length, recording the presentation, and broadcasting the presentation.

A virtual reality and augmented reality system configured for meditation applications including a headset comprising an adjustable strap and a display. The headset further includes heating and cooling elements to provide an immersive virtual reality environment to a wearer. The virtual reality system is operable to determine the physiological condition of the wearer and adjust the virtual reality environment based on the physiological condition of the wearer.

Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.

Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.