An arousal support device including a processor programmed to output a dialogue speech in a form of a question, and obtain response speech which is a response of a driver to the dialogue speech; measure a response time from when the dialogue speech is output till the response speech is obtained; store the measured response time in a database; derive an estimated value of wakefulness of the driver based on the measured response time and a plurality of response times previously stored in the database; and output a signal corresponding to the estimated value to provide arousal support.

A machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.

A machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.

Described herein is a method (800) and system for controlling one or more illumination devices in an eye tracker system (100) such that a measured pupil/iris contrast exceeds a predefined minimum pupil/iris contrast. The method (100) includes: a. capturing images of a subject (102), including one or both of the subject's eyes, during predefined image capture periods; b. illuminating, from one or more illumination devices (108 and 110), one or both of the subject's eyes during the predefined image capture periods, wherein at least one illumination device (108 and 110) is located sufficiently close to a lens of the camera to generate bright pupil effects; and c. selectively varying the output power of at least one of the illumination devices (108 and 110) to generate a bright pupil reflection intensity such that a measured pupil/iris contrast in a captured image exceeds a predefined minimum pupil/iris contrast.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

Disclosed herein is a vehicle theft prevention device. The device can include a data store including event configuration data. The device can include one or more sensors that can sense various types of measurements proximate to a vehicle. The device can include a computing device in communication with the sensors. The computing device can read measurements from the sensors and determine that a particular event has occurred. The computing device can analyze the measurements to determine the particular event occurred based on the event configuration data. When the particular event occurs, the computing device can perform one or more remedial actions.

A method for influencing a system for monitoring alertness of an operator when operating a device includes providing one or more representations of features of the operator in a detection region of the system, where each of the one or more representations reproduces at least one feature of the operator and where the influencing is based on the detection of the one or more representations by the system.

System, methods, and other embodiments described herein relate to adapting notifications according to monitoring states of a vehicle operator. In one embodiment, a method includes acquiring sensor data associated with an operator of a vehicle. The method also includes determining an operator state from the sensor data. The method also includes computing a difference of the operator state to a parameter associated with monitoring a component in the vehicle. The method also includes adapting a notification associated with the component by a controller according to the difference.

A gaze target detector includes a line-of-sight detector, a relative speed data acquiring unit, a relative position data acquiring unit, a curvature calculator, a threshold adjuster, and a gaze determination unit. The line-of-sight detector detects the line of sight of an occupant in a vehicle. The relative speed data acquiring unit acquires a relative speed between the vehicle and a gaze target at which the occupant is gazing. The relative position data acquiring unit acquires a relative position between the vehicle and the gaze target. The curvature calculator calculates the curvature of a traveling track of the vehicle. The threshold adjuster adjusts a threshold based on at least one of the relative speed, the relative position, or the curvature. The threshold is used to determine the gaze target. The gaze determination unit determines the gaze target as a gaze event based on the threshold adjusted.