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.

In some embodiments, apparatuses, systems, and methods are provided herein useful to detecting a deviation in a person's activity. In some embodiments, an apparatus comprises one or more sensors, the one or more sensors configured to monitor parameters associated with a person and the person's home, and a control circuit, the control circuit communicatively coupled to the one or more sensors and configured to receive, from the one or more sensors, values associated with the parameters, create, based on the values associated with the parameters, a spectral profile for the person, determine, based on the spectral profile and a routine base state for the person, that a combination of the values indicates a deviation, determine, based on the deviation, an alert, and cause transmission of the alert.

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.

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.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

Disclosed are systems and methods for providing distributed security event monitoring. The system can include a central monitoring system and sensor devices positioned throughout a premises that passively detect conditions and emit signals guiding people on the premises when a security event is detected. The sensor devices can include suites of sensors and can transmit detected conditions to the central monitoring system. The central monitoring system can combine the detected conditions to generate a collective of detected conditions, determine whether the collective of detected conditions exceeds expected threshold conditions, identify a security event on the premises based on the collective of detected conditions, classify the security event using machine learning models, generate instructions to produce audio or visual output at the sensor devices that notifies people on the premises about the security event, and transmit the instructions to the sensor devices to emit signals indicating information about the security event.

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

A monitoring system a user activity sensor to determine patterns of activity based upon the user activity occurring over time.

Various embodiments described herein relate to an intelligent warning system in a communication service. One embodiment of the present invention analyzes a user's biometric signals and image data of one of the users in a communication session to determine a distress level. In addition, location data indicating the user's velocity and direction can be used to determine a distress level. If the biometric signals or the image data indicate a distress level that meets or exceeds a threshold level, a notification is automatically sent to a remote user. This can allow a remote user to render assistance to a user in distress, even when the user is unable to communicate one or more conditions.