An arousal level prediction apparatus and method are disclosed. The arousal level prediction apparatus obtains first biological information indicating current biological information of the user, obtains first environment information indicating a current environment around the user, and obtains living information of the user indicating an activity history of the user. The arousal level predication apparatus includes a process that calculates a first arousal level indicating a current arousal level of the user based on the first biological information, predicts a second arousal level, which is an arousal level of the user at a certain period of time later, based on the first arousal level, the first environment information and the living information, and outputs the second arousal level.

Disclosed is a biosensor arrangement structure, including: a plurality of biosensors that is disposed in a seat for supporting an occupant and that measures a health condition of the occupant. The seat includes: a seat body for holding the occupant; and an auxiliary supporter for supporting a body part of the occupant except for a torso and thighs. At least one of the plurality of biosensors is disposed at the auxiliary supporter.

An autonomic nerve control device includes an obtainer that obtains a physiological quantity of a user before the user occupies a moving body, and a controller that controls the autonomic nerves of the user occupying the moving body based on the physiological quantity of the user obtained by the obtainer.

A system for transdermal alcohol sensing to be worn near a skin surface of a user, including: an alcohol sensor; a microporous membrane; a housing coupled to the alcohol sensor and the membrane, defining a volume between the alcohol sensor and a first membrane side, and fluidly isolating the volume from a second membrane side opposing the first membrane side; an electronics subsystem electrically coupled to the alcohol sensor, operable to power and receive signals from the alcohol sensor; and a fastener operable to position the second membrane side proximal the skin surface.

It is provided a wearable device for determining when a user has fallen down. The wearable device comprises: a first biometric sensor for obtaining first biometric data of the user, wherein the first biometric sensor is a first accelerometer configured to measure acceleration of a part of a first limb of the user; a second biometric sensor for obtaining second biometric data of the user comprising a finger pressure parameter; and a third biometric sensor for obtaining third biometric data, the third biometric sensor being a second accelerometer configured to measure acceleration of a body part of the user being distinct from the first limb. The wearable device is configured to determine an identity of the user is based on the first biometric data, the second biometric data and the third biometric data, the identity being used to control access to a physical space, and to determine when the user has fallen down.

The invention relates to a device for determining the driving behavior of a driver, the device comprising at least means for receiving data from two or more data sources, of which at least one produces data relating to changes in the state of motion of a vehicle and at least one other produces measured data on the well-being of the driver; means for scoring the received data by comparing it with data-specific reference values; means for forming a respective sub-index from each scored item of data; means for determining a driving behavior index on the basis of the formed sub-indices; and means for controlling control equipment of the vehicle on the basis of the driving behavior index and/or for storing the driving behavior index in a database.

An alcohol level detection device capable of detecting an alcohol level of a driver, includes a thermal camera mounted near a driver seat and to detect a body temperature of the driver, a contact electrocardiogram sensor disposed on a steering wheel steered by the driver and to detect an electrocardiogram waveform and a heart rate of the driver, a non-contact gas detection sensor disposed on a grip of the steering wheel and to detect an alcohol component contained in a gas discharged from a skin of the driver without contact with the skin, and a determination circuit which determine the alcohol level of the driver based on a measurement value of the body temperature detected by the thermal camera, measurement values of the electrocardiogram waveform and the heart rate detected by the contact electrocardiogram sensor, and a value of the alcohol component detected by the gas detection sensor.

A sleepiness prediction system includes a lifelog obtainer, a route information obtainer, and a sleepiness predictor. The lifelog obtainer obtains a lifelog including at least a get-up time of an occupant of a vehicle and a boarding time at which the occupant boards the vehicle. The route information obtainer obtains route information regarding a route to a destination of the vehicle. The sleepiness predictor predicts a sleepiness level of the occupant while the occupant is in the vehicle, based on the lifelog and the route information.

Embodiments of the disclosure provide a system for predicting onset of sleep. The system includes a user worn device that includes a plurality of primary sensors to collect a plurality of primary data of a user, a first processor, in communication with the primary sensors, to receive the data and further process the data, wherein the processor determines an onset of sleep data in the user, a first communications module, in communication with the processor, transmits the onset of sleep data, a secondary device, that collects secondary data of the user, a secondary processor, that receives the onset sleep data from the first communications module and secondary data from the at least one secondary sensor, and combines the received onset sleep data and the secondary data to determine whether an onset of sleep event is possible and warn the user about the onset of sleep event.

A system for monitoring and analyzing impact data to determine consciousness level and injuries, comprising: impact event monitoring device monitors impact event that occurs to first end-user, impact event monitoring device integrated into objects; impact event monitoring device collects impact data of objects, first end-user using sensors, impact sensing unit, motion detection unit, GPS module, image capturing unit; network module send impact data to impact event monitoring and analyzing module, impact event monitoring and analyzing module convert impact data into Yaw, Pitch and Roll data, impact event monitoring and analyzing module analyze Yaw, Pitch and Roll data with machine learning techniques and deep learning techniques; impact event monitoring and analyzing module report and send emergency notifications to second computing device, impact data accessing module enable second end-user to examine location and intensity of impact event to understand consciousness level and injuries for providing better treatment to first end-user.