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.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

Systems and methods for detecting onset, presence, and progression of particular states, including intoxication, include observing eye movements of a subject and correlating the observed movements to known baseline neurophysiological indicators of intoxication. A detection system may record eye movement data from a user, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not intoxication or impairment is present. The detection system may alert the user to take corrective action if onset or presence of a dangerous condition is detected. The eye movements detected include saccadic and intersaccadic parameters such as intersaccadic drift velocity. Measurements may be collected in situ with a field testing device. An interactive application may be provided on a user device to provoke the desired eye movements during recording.

Controlled-environment facility resident behavioral and/or health monitoring may employ controlled-environment facility resident wearables each having a band configured to be affixed around a portion of a controlled-environment facility resident, irremovable by the resident and may include sensor(s) configured to measure biometric(s) of the controlled-environment facility resident and one or more physical parameter(s) experienced by the wearable, with a transmitter transmitting the biometric(s) and/or the physical parameter(s) to a controlled-environment facility management system. The controlled-environment facility management system may predetermine one or more normal input levels of the biometric(s) and/or physical parameter(s), receive the transmitted biometric(s) and/or physical parameter(s), determine whether received biometric(s) and/or physical parameter(s) rises above or falls below the predetermined normal input level(s), and alert controlled-environment facility personnel and/or law enforcement when received physical parameter(s) and/or received biometric(s) rise above or fall below the predetermined normal input level(s).

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.

A method for monitoring a patient in a bed using a camera. The method includes identifying a boundary of the bed using data from the camera, identifying parts of the patient using data from the camera, and determining an orientation of the patient using the parts identified for the patient. The method further includes monitoring movement of the patient using the parts identified for the patient and computing a departure score indicating the likelihood of the patient departing the bed based on the orientation of the patient and the movement of the patient. The method further includes comparing the departure score to a predetermined threshold and generating a notification when the departure score exceeds the predetermined threshold.

A system and method is provided for monitoring a biological substance in a bodily orifice. The system includes a wearable device configured to be worn in a bodily orifice. A biosensor is carried by the wearable device and is constructed and arranged to obtain raw data regarding a biological substance in the orifice. The biosensor includes a processor circuit to provide processed data from the raw data, and a transmitter to wirelessly transmit the processed data to a second device.

A surgical item managing method for use in a smart operating room to manage a surgical item used during a surgical procedure is provided. The surgical item includes a flexible RFID tag. The method includes the steps of: obtaining an information about a position of the flexible RFID tag; photographing a patient to obtain a position of the patient; determining if the position of the flexible RFID tag and the position of the patient overlap, to determine if the position of the flexible RFID tag is in the body of the patient; and giving a warning when the position of the flexible RFID tag is in the body of the patient.

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 apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.