An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.

An apparatus device may include a bio-impedance sensor configured to take a bio-impedance measurement from a body of an individual, an optical sensor configured to take an optical measurement from the body of the individual, and a processing device configured to receive a first bio-impedance measurement from the bio-impedance sensor taken during a first period of time and a first optical measurement from the optical sensor taken during the first period of time, receive first location information of the individual during the first period of time, determine a first correlation between a physiological parameter and at least one of the first location, the first bio-impedance measurement, or the first optical measurement, and determine a first level of the physiological parameter based on the first correlation.

Systems and methods for enhancing infection detection and monitoring through decomposed physiological data are disclosed. An example method includes receiving, from a wearable device of a user, physiological data of the user and decomposing the physiological data, by applying a heart rate algorithm, to generate one or more physiological parameters. The example method further includes analyzing, by applying the heart rate algorithm, the one or more physiological parameters to output a period classification, and determining whether or not the period classification is indicative of an infection. The example method further includes, responsive to determining that the period classification is indicative of the infection, displaying, in a user interface, a warning to the user that indicates the infection, and receiving, from the wearable device of the user, additional physiological data of the user to monitor the infection.

A method and system are provided for estimating a physiological parameter using a parameter model determined by a deep neural network. An example method includes training a deep neural network with indirect and direct physiological parameters from a user database. The medical parameters include a respiratory rate, oxygen saturation, temperature, blood pressure, and pulse rate. The method includes determining if a new user belongs in a group. If the parameter model estimated physiological parameter using the closest group to the new user and associated calibration, then the method quantizes the parameter inputs to determine which physiological parameter a new user is most sensitive and to determine a new group and calibration coefficients or curves for the new user.

An adjustable measurement device is described that may include a housing, a power supply, a processor, a communication device, an elastic coupling member, a physiological sensor, and/or a clamp. The housing may be configured to attach to a wearable band that is wearable by a subject. The housing may include a chamber within the housing. The power supply, the processor, the communication device, the elastic coupling member, and or the physiological sensor may be disposed within the chamber. The elastic coupling member may couple the physiological sensor to the housing. A force exerted by the elastic coupling member on the physiological sensor may be in a direction through an opening towards a body part of a subject. As the subject wears the wearable band and the housing is coupled to the wearable band, the physiological sensor may be adjacent to or contact the subject.

The present invention relates to an electrocardiogram measurement apparatus (measurement sensor) which can be used in combination with a smartphone by an individual. The electrocardiogram measurement apparatus according to the present invention comprises: two amplifiers for receiving electrocardiogram signals from a first electrode and a second electrode; one electrode driving unit; a third electrode for receiving an output of the electrode driving unit; an A/D converter connected to an output terminal of each of the two amplifiers and converting analog signals into digital signals; a microcontroller for receiving the digital signals from the A/D converter; and a communication means for transmitting the digital signal, wherein: the microcontroller is supplied with power from a battery; the microcontroller controls the A/D converter and the communication means; and each of the two amplifiers amplifies one electrocardiogram signal so as to simultaneously measure two electrocardiogram signals.

Various embodiments described herein relate to a method and related wearable device and non-transitory machine-readable medium including receiving sensor data from at least one sensor of the wearable device; comparing the sensor data to schedule format stored in a memory of the wearable device, wherein the schedule format specifies at least one characteristic of sensor readings previously associated with a predefined context; determining that the received sensor data matches the schedule format; determining, based on the received sensor data matching the schedule format, that the user is currently in the predefined context; identifying an action associated with the predefined context; and executing the action while the user is in the predefined con text.

A telemedical interface pressure monitoring system is provided for intermittent or continuous monitoring of the pressure that occurs at the interface between the body and a support surface such as with a compression device, cast or resting surface. The system simultaneously measures interface pressure at multiple compression positions as well as provide real-time measurement data to both patients and clinicians. The system uses an array of one or more sensors and a data collection and transmission node with a microprocessor and transmitter/receiver that transmits the sensor data to a receiver such as a mobile device or cloud or clinic server for remote display, evaluation and automatic recording. Remote receivers can also control compression devices associated with the node.

Sensing devices including sensors such as flexible and stretchable fabric-based pressure sensors, may be associated with or incorporated in garments, such as socks, intended to be worn against a body surface (directly or indirectly). Specific manifestations of a sensing system incorporated in a sock substrate are described in detail. Dedicated electronic devices interface electrically with sensors through intermediate conductive traces, optional conductive bridges, conductive contacts provided in a mounting tab.

Embodiments of the present invention disclose a method for detecting a wearable device, and the wearable device, where the method includes: detecting a value of a distance between the wearable device and a user; determining whether the value of the distance between the wearable device and the user exceeds a preset distance threshold; if the value of the distance between the wearable device and the user does not exceed the preset distance threshold, detecting a body feature value of the user within a preset time period; and if the body feature value of the user does not exceed a preset threshold range of the body feature value of the user, determining that the user has worn the wearable device.