Embodiments relate to a device and method for determining a status of an environment and/or a status or condition of a person therein. The method may comprise: receiving an output of a sensor to monitor said environment; commencing a time window after the sensor detects activity in the environment; upon expiry of the time window, activating an active reflected wave detector to measure wave reflections from the environment, the detector consuming more power in an activated state than the sensor in an activated state; and determining a status of the environment and/or of a person therein based on an output of the active detector indicative of one or more of the measured reflections. The method comprises delaying expiry of the time window in response to the sensor detecting activity in the environment during the time window.

A fall detector device (102) for mounting on a wall for monitoring an environment (100), comprising: a motion sensor (204) for detecting motion of a person (106) within a first field of view of the motion sensor; an active reflective wave sensor (206) for detecting the presence of a person within a second field of view of the active reflective wave sensor using wave reflections from the environment, the first and second fields of views at least partially overlapping one another, and a processor coupled to the motion sensor and active reflective wave sensor for receiving output from each of the motion sensor and active reflective wave sensor, wherein operation of the active reflective wave sensor is dependent on a detection of motion of a person within at least a portion of the first field of view by the motion sensor.

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 motion-sensing floor mat, an assembly of such floor mats, and a monitoring system with such floor mats are provided; wherein the floor mat can be joined with another such floor mat and electrically connected to a monitoring device to form the monitoring system; the monitoring device stores a queue list and a topology matrix and uses a topological algorithm to store the identification tag of each such floor mat detected into the queue list in order, to gradually establish the topology matrix for the floor mats detected; and to thereby obtain the relative positions of the floor mats detected. When any of the floor mats is subjected to pressure (e.g., when someone falls on the floor mat accidentally) and generates a sensing signal, the monitoring device can pinpoint the position of that floor mat (i.e., the location of the fall) rapidly according to the topology matrix.

An overdose of opioids can cause the user to stop breathing, resulting in death. A physiological monitoring system monitors respiration based on oxygen saturation readings from a fingertip pulse oximeter in communication with a smart mobile device and sends opioid monitoring information from the smart mobile device to an opioid overdose monitoring service. The opioid overdose monitoring service notifies a first set of contacts when the opioid monitoring information.

The present invention relates to non-invasive method and system for characterising and certifying cognitive activities by detecting gaseous substances emitted by an organism, by means of the respiration, perspiration, and/or secretion, and changes measureable by sensors during said cognitive activities. Substance detection makes it possible to characterise the olfactory signal in order to determine and certify whether or not a cognitive activity has occurred and to classify said signals into different categories of cognitive activities.

Emotional loneliness is referred as the absence of an attachment figure in one’s life and someone to turn to. The existing methods use installation of sensors for tracking the movement, behaviour and activity of the person, but most of the efforts are obtrusive in nature. A non-obtrusive method and system for detection of emotional loneliness of a person have been provided. The disclosure is utilizing multiple varied techniques to understand the emotional loneliness. The multiple techniques comprise room change movement anomalies, living room stay anomalies, correlating the living room stay with the bedroom stay and outdoor movement anomalies. The methodology also ensures reduced variance and false positives, as emotional loneliness is finally determined based on more than two positives of above methods. The detection of person’s movement is done using a featured engineered dataset based on collection of raw time series data collected from a plurality of motion sensors.

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).

In one embodiment, a device obtains sensor data from one or more sensors worn by a first responder. The device analyzes the sensor data to identify a position of a person with whom the first responder is interacting. The device determines that a policy violation has occurred based in part on the position of the person. The device provides an alert that indicates the policy violation.

A millimeter wave radar apparatus determining a fall posture is applied to a human body. The millimeter wave radar apparatus includes a microprocessor and a millimeter wave radar. The millimeter wave radar is electrically connected to the microprocessor. The millimeter wave radar is configured to transmit a radar wave to the human body. The millimeter wave radar is configured to receive a reflected radar wave reflected from the human body based on the radar wave. The microprocessor is configured to obtain a point cloud information based on the reflected radar wave. The microprocessor is configured to utilize the point cloud information to determine whether the human body is in the fall posture.