Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.

A sleep monitor for monitoring baby sleep uses sleep state classification based on heartbeat feature respiration features. The sleep monitor automatically retrains the classification during use of the sleep monitor. Training examples for use in this training process are generated automatically by detecting time instants whereat the baby in the bed is in a wake state, based on signals from the at least one of a sound feature detector a movement feature detector (112) and an open eye detector (114). The retraining may comprise using time sequence from the end of detection of wake states to assign a class to heartbeat feature and/or respiration feature values during that time sequence for the training process. In an embodiment, the retraining comprises clustering detected heartbeat feature and/or respiration feature values detected outside the detected wake states.

Systems and methods for detecting a traumatic brain injury (TBI). The system comprises a sensing arrangement and a control unit. The sensing arrangement collects eye movement data of a user. The control unit is in communication with the sensing arrangement and configured to compare the eye movement data to one or more baseline measurements of eye movement dynamics. The control unit is also configured to generate an alert indicating the presence or severity of the TBI for delivery to control unit administrator if the eye movement data diverges from one or more of the baseline measurements by a threshold amount.

According to some aspects, a device is presented herein that is configured to be located underneath an eyelid and worn by a user for treating dry eye. The device includes a first surface configured to face a portion of a sclera of the eye, and a second surface configured to face an eyelid and to be completely covered by the eyelid. In some embodiments, the device further includes a plurality of stimulation electrodes proximal to the first surface, wherein the plurality of stimulation electrodes is configured to stimulate the sclera. The device further includes an energy storage element coupled to the plurality of stimulation electrodes. The energy storage element is configured to supply power to the plurality of stimulation electrodes. The device further includes a processor configured to control a supply of energy from the energy storage element to the plurality of stimulation electrodes to stimulate the sclera.

A method of updating a protocol for a Virtual Reality (VR) medical test via a user device having a processor, the VR medical test being performed on a subject via a VR device worn by the subject, wherein the method is performed by the processor and the method comprises: displaying GUI elements associated with the protocol on the user device, the GUI elements having user adjustable settings for modifying a functioning of the VR medical test; receiving a selection input from the user device corresponding to a selection of the GUI elements; receiving a setting input from the user device that corresponds to the selected GUI elements; modifying the user adjustable setting for each of the selected GUI elements according to the corresponding setting input; and updating the protocol based on the user adjustable setting for each of the selected GUI elements and operations associated with the VR device.

A method is implemented by a computing device for helping a particular user use a user interface (UI). Electroencephalography (EEG) data is obtained that indicates brain activity of a particular user during a period in which that user views the UI and/or interprets help information that describes how to use the UI. Based on the EEG data, the computing device selects, from among multiple predefined cognitive states, the one or more cognitive states that characterize the particular user during the period. The computing device assists the particular user to use the UI by customizing the help information for the particular user based on the one or more selected cognitive states. A complementary computing device and computer program product are also disclosed.

Unlike state of art eye blink detection techniques that are generalized for usage across individuals affecting accuracy of eye blink prediction from subject to subject, embodiments of the present disclosure provide a method and system for personalized eye blink detection using passive camera-based approach. The method first generates a subject specific annotation data, which is then further processed to derive subject specific personalized blink threshold values. The method disclosed provides three unique approaches to compute the personalized blink threshold values which is one time calibration process. The personalized blink threshold values are then used to generate a binary decision vector (D) while analyzing input test images (video sequences) of the subject of interest. Further, values taken by elements of the decision vector (D) are analyzed for a predefined time period to predict possible eye blinks of the subject.

A healthcare apparatus includes a ballistocardiogram (BCG) sensor configured to sense a ballistocardiogram signal of a subject, a camera configured to acquire a color facial image, and a processor configured to detect a region of interest (ROI) from the color facial image, to detect a first color image of a forehead area to acquire a first black and white image, to detect a second color image of a cheek area to acquire a second black and white image, to apply the first and second black and white images to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject, to calculate a first heart rate from the BCG signal waveform, to calculate a second heart rate from the remote PPG signal waveform, and to output a heart rate of the subject based on the first heart rate and the second heart rate.

A system and/or method for diagnosing a neurologic condition. The system comprises a wearable head orientation sensor, a wearable eye imaging module, and a display with a visual target. The head orientation sensor is responsive to pitch or yaw head orientation information. The eye imaging module is configured for imaging a characteristic of the retina, sclera, cornea, limbus, or pupil to determine an eye position or eye movement. An electronic circuit in the system determines an ocular parameter measurement in response to the head orientation sensor and the eye imaging module. The neurologic condition is diagnosed in response to the ocular parameter measurement.

The disclosed embodiments provide systems and methods for predicting presence of one or more neurological deficits. The system may include a microphone, a camera, one or more memory devices storing instructions, and one or more processors configured to execute the instructions to extract audio information including a period density entropy coefficient and a mel frequency cepstral coefficient from an audio feed received from the microphone. Additionally, the instructions may cause the processor to determine position and depth information of eye movement from a video feed received from the camera and detect features of interest including facial landmarks, spatial orientation of limbs, and positional information of limb movements from the video feed. The one or more processors may further extract the features of interest from the video feed and process the extracted features of interest by aligning the extracted features of interest to a common reference.