A shovel includes a lower traveling body, an upper turning body turnably mounted on the lower traveling body, a cab mounted on the upper turning body, an operating information obtaining device configured to obtain operating information, and a processor configured to store the operating information. The processor is configured to obtain the biological information of an operator in the cab and to correlate the biological information and the operating information.

An error monitoring apparatus and method are provided. The error monitoring method includes determining whether or not a predetermined event has occurred to a first mobility; sensing to collect an event-related potential (ERP) for at least one passenger of the first mobility for a predetermined amount of time, analyzing the collected ERP based on the determination, and transmitting error information of the first mobility to a traffic control server based on an analysis result. Herein, the predetermined event includes a traffic accident related at least to the first mobility, and the error information of the first mobility includes at least one of time information regarding when the ERP occurs, a waveform of the ERP, location information of the first mobility, or operational information of the first mobility.

An electronic device 10 includes an image-capturing unit 11, a line-of-sight detector 12, and a controller 14. The image-capturing unit 11 generates an image corresponding to a view by performing image capturing. The line-of-sight detector detects a line of sight of a subject with respect to the view. The controller 14 estimates an alertness level of the subject based on an image and a line of sight. The controller 14 functions as an estimator. The estimator is constructed based on learning data obtained by machine learning a relationship between a learning image, a line of sight of a training subject with respect to the learning image, and biological information relating to an alertness level of the training subject.

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 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 method for determining a level of drowsiness of a driver of a motor vehicle on the basis of a predetermined image analysis algorithm, the vehicle including a camera and a computer, the computer implementing the predetermined algorithm on the basis of a set comprising at least one parameter relating to the attitude of the driver, the method, implemented by the computer, including a phase of learning and a phase of monitoring the state of the driver.

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.

A health risk assessment system for estimating a health risk posed by transporting a user in a vehicle. The system includes one or more processors and a memory communicably coupled to the one or more processors and storing a health risk assessment module. The health risk assessment module includes computer-readable instructions that when executed by the one or more processors cause the one or more processors to attempt to acquire information regarding a travel history of the user. The module is also configured to, using acquired travel history information, determine a value of a first health status parameter and to compare the value of the first health status parameter to a first threshold. Responsive to the value of the first health status parameter exceeding the threshold, the module controls operation of the vehicle to implement at least one infection prevention measure.

Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.

A vehicle includes a processing device that detects an authentication device approaching the vehicle, determines a user's impaired state based on user condition information acquired using at least one device mounted on the vehicle, and performs a safe driving assistance service. The safe driving assistance service can prevent an impaired user from operating the vehicle.