Disclosed is a capacitive sensor (100) for locating the presence of an individual and/or of an object, the sensor (100) including:—a first layer (C1) including at least one first electrode (E1i, i∈[1,N]) extending in a first direction (d1);—a second layer (C2) having at least one second electrode (E2j, j∈[1,M]) extending in a second direction (d2); in which the first direction (d1) is different from the second direction (d2), and in which the first layer (C1) is electrically insulated from the second layer (C2).

A system for monitoring a patient in a patient area having one or more detection zones, the system comprising one or more cameras, a user interface, and a computing system configured to receive a chronological series of frames from the one or more cameras, identify liquid candidates by comparing a current frame with a plurality of previous frames of the chronological series, determine locations of the liquid candidates, identify thermal signatures of the liquid candidates, determine types of liquids of the liquid candidates based on the locations and thermal signatures of the liquid candidates, and generate an alert with the user interface corresponding to the determined types of liquids.

A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.

A system and a method are described for monitoring a medical care environment. In one or more implementations, a method includes identifying a first subset of pixels within a field of view of a camera as representing a bed. The method also includes identifying a second subset of pixels within the field of view of the camera as representing an object (e.g., a subject, such as a patient, medical personnel; bed; chair; patient tray; medical equipment; etc.) proximal to the bed. The method also includes determining an orientation of the object within the bed.

A bed-leaving prediction device (server device) (10) is connected through a digital communication network (60) to: a portable information processing terminal (40) of care staff; environmental sensors (32 to 34) for detecting environment values such as temperature in a room; a human sensor (31); and a bed sensor (35). A bed-leaving prediction processing section (115) calculates a bed-leaving prediction value indicative of a degree of possibility that a care recipient leaves a sleeping furniture after a second time interval has expired since a current time point based on a plurality of environment values detected in a time period between the current time point and a time point before expiration of a first time interval, outputs of the human sensor, and outputs of the bed sensor. A bed-leaving notification processing section (117) compares the bed-leaving prediction value with a threshold value, and transmits, to the portable information processing terminal, a bed-leaving notification indicating that the care recipient leaves the sleeping furniture after the second time interval expires when the bed-leaving prediction value exceeds the threshold value.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

Various arrangements for performing fall detection are presented. A smart-home device (110, 201), comprising a monolithic radar integrated circuit (205), may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person (101) has occurred. Speech may then be output announcing that the fall has been detected via the speaker (217) of the smart home device (110, 201).

A health management system includes a risk calculator 301 for receiving input of vital data on a human or an animal, and for calculating a disease occurrence risk in the human or the animal; and a reduction measure specifier 302 for specifying a reduction measure for reducing the disease occurrence risk in the human or the animal.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

An estimation system includes: a first sensor that detects a first amount of activity that is an amount of activity of a subject in a room; a second sensor that detects a second amount of activity that is an amount of activity of the subject on a bed in the room; and an estimation device that estimates at least one of a position and an action of the subject in association with a position in the room based on the first amount of activity detected by the first sensor and the second amount of activity detected by the second sensor, and outputs an estimation result obtained from the estimation. The first sensor and the second sensor are sensors other than two-dimensional image sensors.