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.

An apparatus for generating ECG recordings and a method for using the same are disclosed. The apparatus includes a handheld device having four electrodes on an outer surface thereof, the handheld device having an extended configuration and a storage configuration. The apparatus also includes a controller configured to measure signals between the electrodes to provide signals that are used to generate an ECG recording selected from the group consisting of standard lead traces and precordial traces. When the handheld device is in the extended configuration and the first and second electrodes contact a first hand of a patient such that the first and second electrodes contact different locations on the first hand, the third electrode is in contact with a location on the patient's other hand and the fourth electrode contacts a point on the patient's body that depends on the particular trace being measured.

Provided are a vehicle outside information acquiring unit to acquire vehicle outside information, a face information acquiring unit to acquire face information, a biological information acquiring unit to acquire biological information, a vehicle information acquiring unit to acquire vehicle information, a vehicle outside information feature amount extracting unit to extract a vehicle outside information feature amount on the basis of the vehicle outside information, a face information feature amount extracting unit to extract a face information feature amount in accordance with the vehicle outside information feature amount, a biological information feature amount extracting unit to extract a biological information feature amount in accordance with the vehicle outside information feature amount, a vehicle information feature amount extracting unit to extract a vehicle information feature amount in accordance with the vehicle outside information feature amount, and a cognitive function estimation unit to estimate whether a cognitive function of a driver is low on the basis of a machine learning model, the vehicle outside information feature amount, and at least one of the face information feature amount, the biological information feature amount, or the vehicle information feature amount.

A biological information detector detects biological information of an object person. The biological information detector includes a reception antenna that receives radio waves transmitted from a transmission antenna through the object person, and a signal acquisition unit that acquires a reception signal corresponding to a radio wave received by the reception antenna when the radio waves are transmitted from the transmission antenna. The biological information detector further includes a calculation unit that calculates the biological information based on the received signal acquired by the signal acquisition unit. The calculation unit extracts a signal component generated due to movement of a heart of the object person based on the received signal, and calculates a blood pressure of the object person as the biological information based on the signal component.

In one embodiment, a system includes, but is not limited to, an aviation head-mounted communication device including at least: a speaker, a physiological sensor configured to obtain physiological data, and a wireless communication interface; and a smartphone, a smartwatch, or tablet device wirelessly linked to the wireless communication interface and configured to receive the physiological data and output the physiological data on a display.

A rider interface for a vehicle includes a data processor configured to facilitate communication between a rider using the rider interface and the vehicle, the vehicle and the rider interface communicating location and orientation of the vehicle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of the rider using the rider interface, the augmentation responsive to a registration of the communicated location and orientation of the vehicle, wherein at least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the rider interface.

A system for transportation includes a vehicle, a rider occupying the vehicle, and a hybrid neural network. The hybrid neural network includes a first neural network to process a sensor input corresponding to the rider to determine an emotional state of the rider, and a second neural network to optimize at least one operating parameter of the vehicle to improve the emotional state of the rider.

A system for transportation includes a vehicle occupied by a rider, an electronic commerce system of the vehicle, and an artificial intelligence system for processing data from an interaction of the rider with the electronic commerce system to classify a rider state and optimizing at least one operating parameter of the vehicle to improve the rider state.

A motorcycle helmet includes a data processor configured to facilitate communication between a rider wearing the helmet and a motorcycle, the motorcycle and the helmet communicating location and orientation of the motorcycle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation of the motorcycle At least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the motorcycle.

A system for transportation includes a vehicle interface for gathering hormonal state data of a rider in the vehicle. The system further includes an artificial intelligence-based circuit that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes. The set of outcomes includes at least one outcome that promotes rider safety. The inducing variation includes control of timing and extent of the variation.