A monitoring system may include a memory having computer-readable instructions stored thereon and a processor operatively coupled to the memory. The processor may read and execute the computer-readable instructions to perform or control performance of operations. The operations may include receive, prior to a collision involving a vehicle, sensor data representative of a feature of an internal environment and determine the collision has occurred. The operations may include automatically instruct, based on the collision, a sensor to generate another sensor data representative of another feature of the internal environment. The operations may include receive the another sensor data from the sensor and compare the sensor data and the another sensor data to accident data corresponding to previous accidents. The accident data may include a diagnosed injury and an accident severity of each of the previous accidents. The operations may include determine a severity of the collision based on the comparison.

A driver condition estimating device includes circuitry configured to measure movement of the head of a driver from output of a driver camera and detect a sign of abnormality of the driver from the movement of the head. On condition that lateral acceleration acting on the head of the driver is a predetermined value or less, the circuitry is configured to calculate a periodic feature amount from time series data showing the movement of the head of the driver, calculate a time series variation pattern from the obtained periodic feature amount, and compare of the obtained time series variation pattern with a predetermined threshold.

The invention relates to a system that interacts with an occupant of a motor vehicle, comprising: —a measuring device comprising at least one sensor arranged to capture at least one parameter relating to the occupant of said vehicle, —an on-board processing unit using an evaluation model for the emotional state of the occupant, said processing unit being arranged to receive said parameter and to define a data item representative of the emotional state of said occupant using the model, —the representative data corresponding to a point in a three-dimensional space for characterising the emotional state of the occupant, and —at least one actuator configured to activate at least one multi-sensory stimulus for interaction with the occupant, said stimulus allowing the emotional state of said occupant to be altered.

A sensibility meter detects biometric information relating to an operator corresponding to an output from a target appliance, and determines a comfort level of the operator based on the biometric information. A first control unit determines a second target value relating to the output based on a difference between a first target value relating to the comfort level and the comfort level. A second control unit determines a control input to the target appliance based on a difference between the second target value and the output. A δ setting unit performs weighting corresponding to an operation level of the operator, for an operation input to the target appliance by the operator, and for the control input. An adder adds the weighted operation input and control input, and inputs the resultant to the target appliance.

A monitoring device comprising at least one measurement module for measuring at least one physiological parameter of the crew member, at least one consolidation module for consolidating the measured physiological parameter or parameters, a fusion module for fusing the consolidated physiological parameter or parameters in order to detect at least one physiological status of the crew member, a filtering module for filtering the physiological status or statuses, a determination module for determining a level of incapacity of the crew member, a transmission module for transmitting a signal indicative of the level of incapacity of the crew member to a user device.

A system and method are provided for detecting and continuously monitoring Pupillary Light Reflex responses and neurological conditions to determine probability and degree of impairment to a user due to intoxication or neurological disorder. In one embodiment, the system includes a camera in a housing affixed to an article worn on the head of a user operable to enable the camera to view at least one eye of the user, a portable video capture device (VCD) wired or wirelessly coupled, or integrated to the camera to receive video therefrom, and software executed by a processor in the VCD. The software includes modules to locate and measure a change in a feature of the eye, extract data therefrom and predict a degree of impairment of the user, and output the probability and degree of impairment to the user and/or to a third party monitor. Other embodiments are also disclosed.

A drowsiness prediction device includes: a carbon dioxide concentration detector that detects the concentration of carbon dioxide in a compartment; an oxygen saturation detector that detects oxygen saturation in the body of a driver present in the compartment; a predictor that predicts, according to the concentration of carbon dioxide and the oxygen saturation detected, a level of drowsiness that the driver would feel after the detection of the concentration of carbon dioxide and the oxygen saturation; and an outputter that outputs information indicating the predicted level of drowsiness.

A brain-computer aided analysis method for an aviation accident is provided. The method includes the steps of obtaining historical electroencephalogram (EEG) signals and historical psychological and physiological features of various pilots during flight to be recorded as first EEG signals and first features; training a feature recognition model by using the first EEG signals as an input and the first features as an output; inputting EEG signals of a pilot of an aviation accident aircraft into the feature recognition model, and outputting psychological and physiological features of the pilot of the aviation accident aircraft to be recorded as second features; determining whether the second features are abnormal or not according to the historical psychological and physiological features of the pilot of the aviation accident aircraft and the first features.

The present application is applicable to the field of computer application technology, and provides methods and devices for detecting driver distraction, including: acquiring the EEG data of the driver; preprocessing the EEG data, and then inputting it into a pre-trained distraction detection model to obtain the distraction detection result of the driver; obtaining the distracted detection model by training a preset convolution-recurrent neural network using EEG sample data and corresponding distracted result label; sending the distraction detection result to an in-vehicle terminal associated with the identity information of the driver, wherein the distraction detection result is used to trigger the in-vehicle terminal to generate driving reminder information according to the distraction detection result. When detecting driver distraction, the accuracy and efficiency are improved, thereby reducing the probability of traffic accidents.

Disclosed is a method for classifying signals for movement control of an autonomous vehicle. The method includes receiving first data comprising concurrently recorded electroencephalogram (EEG) and electromyogram (EMG) signals from a user. The data is used to train a classification model based on the recorded signals. The method further involves receiving second data comprising further EEG and EMG signals recorded from the user, comparing the second data to the classification model to determine a user movement represented by the second data, and determining a control signal for controlling the autonomous vehicle, based on the user movement. This may be used in a further method for identifying an event.