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.

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.

An electronic monitoring system implements an evaluation strategy to distinguish between low-interest detected events and high-interest detected events and to send lower or standard alerts to a user device if a detected event is a low-interest event and a heightened alert or warning if the detected event is a high-interest event. The system may utilize patterns of information to establish baseline event characteristics for the particular monitored environment. The baseline event characteristics are used to evaluate triggering events for determining whether those events are best categorized as low-interest or high-interest.

Methods, systems, and apparatus, including computer programs encoded on storage devices, for drone-augmented emergency response services. In one aspect, a monitoring system, comprising: a plurality of monitoring control units, and a monitoring application server, wherein the monitoring application server includes a network interface, one or more processors, and one or more storage devices that include instructions to perform operations. The operations include receiving an emergency event notification from a first monitoring control unit of the plurality of monitoring control units, determining a type of emergency event, and a location associated with the emergency event notification, identifying one or more drones that can be deployed to the location associated with the emergency event, and transmitting an instruction to a monitoring station server associated with a drone base station to deploy the one or more identified drones to the location associated with the emergency event.

Embodiments of a system and method for method for automatically determining pet owner interaction with a pet are generally described herein. A method may include obtaining sensor data including pet behavior metrics from a sensor, aggregating the sensor data, and interpreting the aggregated sensor data to determine an engagement factor of a pet owner to a pet. The method may include outputting a report based on the engagement factor or the aggregated sensor data.

Techniques are described for using sensor data derived from a monitoring system to drive personalized content. Sensor data captured by a monitoring system may be used to determine attributes of users of the monitoring system and/or attributes of a property monitored by the monitoring system. The determined attributes may be used to select content to present to users of the monitoring system. Content presentation may be made through the monitoring system and may be triggered based on events detected by the monitoring system.

Devices to aid in finding items, including purses or other luggage, along with methods of operation of the devices. The resultant movement of an item during use, and lack of movement of an item during non-use, are exploited to alert a user to the whereabouts of the item through adaptive techniques. A device is located with the item, purse, or other luggage, and configured to sense motion information. Location notification may be based on such motion information.

In some implementations, a system can transmit communications indicating an occurrence of a particular type of safety incident experienced by a user. Registration information that indicates that a plurality of safety devices of different types are to be registered with the user is initially obtained. Sensor data from the plurality of safety devices of different types are obtained. An occurrence of a particular type of safety incident experienced by the user is then selected from among a plurality of types of safety incidents. The selection may be based at least on the obtained sensor data and the obtained registration information. A communication is then provided to another user to indicate the occurrence of the particular type of safety incident experienced by the user in response to selecting the occurrence of the particular type of safety incident.

Optimizing and/or personalizing activities to a user through artificial intelligence and/or virtual reality.

Presented is a system and method for monitoring activities of daily living, ADLs, of a person within an environment. The system comprises an ADL inference unit adapted to receive a sensor output signal representative of a detected value of a property of at least one of: the person; and the environment, and to generate an inferred ADL output signal representative of the inferred ADL of the person. A monitor unit is adapted to generate a monitor signal dependent on at least one of: the received sensor output signal; and the inferred ADL output signal.