A method for identifying occupant-specific settings for a vehicle comprises detecting a vehicle occupant; collecting biometric occupant data of the detected vehicle occupant; assembling a descriptor based on at least part of the collected occupant data; storing the descriptor in a database; and updating a cluster of descriptors in the database based on the assembled descriptor, wherein descriptors grouped in the cluster are close to each other, and wherein the cluster corresponds to the detected vehicle occupant. Based on the cluster and assembled descriptor can be assigned to the cluster, and vehicle setting data can be calculated that has been stored in association with the cluster. The vehicle setting data corresponds to occupant-specific settings for the vehicle. A vehicle comprises a system capable of performing such method.

The disclosure provides a system that may receive an identification of a first patient; may receive a first template that includes first multiple locations associated with a face of the first patient and associated with the identification of the first patient; may determine second multiple locations associated with a face of a current patient; may determine a second template of the face of the current patient based at least on the second multiple locations associated with the face of the current patient; may determine if the first template matches the second template; if the first template matches the second template, may provide an indication that the current patient has been correctly identified as the first patient; and if the first template does not match the second template, may provide an indication that the current patient has not been identified.

An in-cell optical biometrics sensor includes: a display unit sets each including one or multiple display units; optical sensing cells respectively disposed in gaps between the display unit sets; and optical modules respectively disposed adjacently to the optical sensing cells, wherein each optical module includes a light shielding layer for shielding stray light, and the optical sensing cells sense biometrics characteristics of an object through the optical modules. Thus, the optical biometrics sensor can be integrated in a display panel to provide partial or full-display optical biometrics characteristics sensing functions.

A vital-data measurement controller is provided for controlling vital-data measurement when a user measures vital data by using a device data measurement. The measurement controller includes a controller that acquires image data including the user’s face and the device by controlling a camera; and an image processor that authenticates the user’s face and the based on the image data. The image processor then determines whether the device is worn by the user based on a positional relationship between the user and the device in the image data, and determines that a period in which the device is determined to be worn by the user after the authentication of the user and the device is a period of a proper state for measurement.

Disclosed are a method and a device for obtaining a safe interval of a human body parameter in a built environment, a terminal device, and a storage medium. The method includes: obtaining a first facial image, a benchmark human body parameter, and a human body posture analysis result of a target user in a target built environment; inputting the first facial image into an emotion analysis model to obtain an emotion analysis result; and obtaining the safe interval of the human body parameter of the target user according to the emotion analysis result, the human body posture analysis result, and the benchmark human body parameter.

System and methods for non-contact video-based patient monitoring are described. The systems and methods may include locating one or more condensation removal elements on a transparent enclosure for removal of condensation that may be obstructing the view between a video camera used to monitor a patient and the patient. The systems and methods may further include steps for monitoring the quality of the video stream and automatically initiating the condensation removal elements when a deterioration in the quality of the video stream is detected.

Various embodiments provide systems and methods for identifying impairment using measurement devices.

A system for transportation includes a vehicle occupied by a rider, and an artificial intelligence system for processing a voice of the rider to classify an emotional state of the rider and optimizing at least one operating parameter of the vehicle to improve the emotional state of the rider.

A biometric determination device includes a detection unit configured to detect a face from an image captured; an acquisition unit configured to acquire information on the temperature of a face region, which is a region in which a face is detected by the detection unit, and the temperature of an extra-facial region, which is a region around the face region; and a determination unit configured to determine on the basis of information acquired by the acquisition unit that a face that is detected is a living body when the temperature of the face region is higher than the temperature of the extra-facial region by greater than or equal to a predetermined threshold, and that a face that is detected is not a living body when the temperature of the face region is not higher than the temperature of the extra-facial region by greater than or equal to the predetermined threshold.

A blood pressure measurement device includes an annular bracelet that comes into contact with the user's wrist; a display that is disposed on the outer surface of the bracelet and includes a display surface; a camera that is disposed on the outer surface of the bracelet, has an optical axis tilted from the direction of a normal to the display surface, and captures images of the user; a pulse wave sensor that is disposed on an inner surface of the bracelet and detects a pulse wave at the user's wrist; and a processing circuit that estimates the user's blood pressure. The processing circuit calculates a first pulse wave timing from a temporal change in luminance in the cheek region in the images, determines a second pulse wave timing from the detected pulse wave, and estimates the blood pressure from a time difference between the first and second pulse wave timings.