A method for preventing and detecting a fall of an individual and implementing a system worn by the individual. The system includes a plurality of sensors and an alarm. The plurality of sensors includes a triaxial accelerometer and a barometric sensor. Each sensor of the plurality of sensors generates a signal and is connected to a microprocessor. The microprocessor is configured to execute a computer program stored in a memory to collect and analyze data issued by the plurality of sensors, and to trigger an alarm in response to an analysis of the data.

A system and/or method for determining human health uses a head-worn apparatus that comprises a head orientation sensor, an eye imaging device, and an electronic circuit. The head orientation sensor is configured for generating an electrical head orientation signal in response to head pitch or head yaw. The eye imaging device is configured for observing an eye feature from the sclera, cornea, iris, or pupil, and generates an eye electrical signal in response to eye position, horizontal eye movement, vertical eye movement, pupil size or eyeblinks at a plurality of times. The electronic circuit is configured for generating an ocular parameter measurement such as saccades, vestibulo-ocular reflex, vestibulo-ocular reflex cancellation, vergence, smooth pursuit, nystagmus, dynamic visual acuity, pupil size, and/or eyeblinks from the head and eye electrical signals. The ocular parameter measurement can be used to determine normal human health, a neurologic disorder, a biochemical health impairment, or a physiologic health impairment.

A method and system for detecting and removing EEG artifacts is disclosed herein. Each source of a plurality of sources for an EEG signal is separated for a selected artifact type. Each source of the plurality of sources is reconstituted into a recorded montage and an optimal reference montage for recognizing the selected artifact type of each source. The sources with artifacts are removed and the remaining sources are reconstituted into a filtered montage for the EEG signal.

A method of monitoring eye strain includes detecting the blink status, the vergence status and the pupil status of a user, and then determining whether the user encounters eye strain according to at least one of the blink status, the vergence status and the pupil status of the user. The method further includes facilitating the user to blink or informing the user of eye strain.

Disclosed techniques include in-vehicle drowsiness analysis using blink-rate. Video of an individual is obtained within a vehicle using an image capture device. The video is analyzed using one or more processors to detect a blink event based on a classifier for a blink that was determined. Using temporal analysis, the blink event is determined by identifying that eyes of the individual are closed for a frame in the video. Using the blink event and one or more other blink events, blink-rate information is determined using the one or more processors. Based on the blink-rate information, a drowsiness metric is calculated using the one or more processors. The vehicle is manipulated based on the drowsiness metric. A blink duration of the individual for the blink event is evaluated. The blink-rate information is compensated. The compensating is based on demographic information for the individual.

Embodiments are related to a system with a headset capable of monitoring psychomotor performance of a user of the headset based on eyelid tracking information. The headset includes a sensor assembly coupled to a frame of the headset, and a transceiver coupled to the sensor assembly. The sensor assembly is configured to track an eyelid of an eye of the user and capture eyelid tracking information. The transceiver is configured to obtain the eyelid tracking information from the sensor assembly and communicate the eyelid tracking information to a secondary device coupled to the headset for processing the eyelid tracking information and determination of sleep information for the user based in part on the processed eyelid tracking information.

Computer-implemented method for determining an eye movement, the method comprising: recording a series of images of an eye of a person with a camera; generating a first signal indicative of eye openness depending on time; and for each point in time, determining whether the first signal indicates an open eye, a closed eye, or is inconclusive as to whether the eye is open or closed, depending on whether the first signal exceeds a predetermined first threshold; characterized in that the determination further depends on one or more predetermined criteria, wherein the predetermined criteria comprise minimum and maximum values for one or more of a blink duration, a number of blinks per minute, a time between blinks, and a maximum blink velocity during a movement of the eyelid when the eye is being opened and/or closed.

A method to measure a cognitive load based upon ocular information of a subject includes the steps of: providing a video camera configured to record a close-up view of at least one eye of the subject; providing a computing device electronically connected to the video camera and the electronic display; recording, via the video camera, the ocular information of the at least one eye of the subject; processing, via the computing device, the ocular information to identify changes in ocular signals of the subject through the use of convolutional neural networks; evaluating, via the computing device, the changes in ocular signals from the convolutional neural networks by a machine learning algorithm; determining, via the machine learning algorithm, the cognitive load for the subject; and displaying, to the subject and/or to a supervisor, the cognitive load for the subject.

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.

The motion sickness estimation device includes an opening and closing detection unit configured to detect an opening/closing state of an eye of a vehicle occupant, a face direction estimation unit configured to estimate an orientation of a face of the occupant, and an occupant state estimation unit configured to calculate a traveling speed of the motion sickness with respect to the occupant based on the opening/closing state of the eye and the orientation of the face.