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.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

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.

An operating unit (2) sets ventilation parameters of a control unit (11) of a ventilator (1) that includes a gas dispensing device (10) for ventilation gases. The operating unit includes a display unit and an ventilation parameters input element (23, 24). Two or more of the parameters are linked via a relation condition stored in a relation storage module (40). A relation monitor (3) includes a deviation detector (31) detecting a transgression of the relation condition during parameter setting, and outputs warning information via a warning unit (32). This avoids a need for an operator to note the sometimes complicated connections and dependencies expressed in the relations during the parameter setting. The warning may be sent before the new setting value is sent from the operating unit to the control unit. Operating safety is increased and a risk of setting errors is minimized.

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.

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

A wearable device and a system that includes the wearable device is provided. The wearable device includes at least one sensor configured to sense a characteristic of a user, a communication interface configured to obtain a first identifier of a vehicle or equipment, and a controller. The controller is configured to monitor the user based on the first identifier or send the first identifier to a monitoring system; determine whether there is a harm possibly occurring to the user of the wearable device based on at least one output from the at least one sensor, or send the at least one output to the monitoring system via the communication interface; and output, after the harm is determined to be possibly occurring, an alarm based on receiving confirmation from the user, or based on not receiving any confirmation from the user within a predetermined amount of time.

The present invention relates to a multi-sensing-based vulnerable social group danger recognition detection method of sensing a person being monitored and residence states in real time in a vulnerable social group residence, and, if an analysis result based on the sensed information corresponds to a dangerous situation, transmitting information thereabout to a guardian or related organizations so as to quickly respond thereto. In addition, the present invention is configured to include operations S100 to S800 of receiving sensing information from a sensing means (100) comprised of one or more sensors, analyzing the sensing information, and transmitting, to a central server (300), a dangerous-situation analysis information result of a person being monitored and an alarm signal according thereto.

The systems and methods may use machine learning models to process device data of user devices and determine device usage behaviors for the users of the user devices based on the device data. The systems and methods may provide relatable insights for the device usage behaviors in a user-friendly manner. The systems and methods may provide actional recommendations that users may take in response to the insights provided to promote healthy device usage behaviors or to prevent or reduce the device usage behavior. The systems and methods may also provide recommendations with access to information or other content related to the device usage behavior.

A disclosed method includes receiving at least an indication of audio data generated based on sound captured by a microphone of a monitoring device. A set of audio event labels are generated by processing the received audio data with a model that includes audio recognition patterns. Each audio event label is generated by matching an audio pattern in the received audio data with an audio pattern of the audio recognition patterns. The method further includes identifying a hazard event type occurring within a time window of the time period by processing the set of audio event labels with a hazard detection model. In response to detecting the hazard event type, a hazard alert is generated and at least an indication of the hazard alert is communicated to an electronic device other than the monitoring device, which is authorized to receive communications from the monitoring device.