Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

There is provided an image analysis system having an image analysis server which analyzes an input image of a monitoring area and detects a state of a specific monitored object. The image analysis server is configured to divide, in the input image of the monitoring area, a portion of an area in which the monitored object is expected to be present into a plurality of grids, generate a trained model trained by associating “1” with an image of a grid in which the monitored object is present and “0” with an image of a grid in which the monitored object is not present for each grid, calculate, for the input image, a confidence of a presence of the monitored object for each grid using the trained model, determine a presence or absence of the monitored object in the monitoring area based on confidences of the plurality of grids, and detect an abnormality when a confidence value in at least one of the grids is inconsistent with the determination result.

An example of a medical equipment and responder management system is configured to register potential responders to an emergency event, receive event information including an event location and an event type, provide an automatic notification that includes the event information and mapping information to first and second mobile computing devices associated with first responder and second responders selected from the potential responders, receive a confirmation that the first responder will bring a first item of registered medical equipment to the event, and provide an indication to the second mobile device that the second responder should proceed to the event without the first item of equipment, where the mapping information indicates a location of the first item of equipment located near a first mobile device current location and the current locations of the first and second mobile devices relative to the locations of the event and the first item of equipment.

The present disclosure relates to techniques for detecting cyber-faults in industrial assets. Such techniques may include obtaining an input dataset from a plurality of nodes of industrial assets and predicting fault nodes in the plurality of nodes by inputting the input dataset to a one-class classifier. The one-class classifier may be trained on normal operation data obtained during normal operations of the industrial assets. Further, the cyber-fault detection techniques may include computing a confidence level of cyber fault detection for the input dataset using the one-class classifier and adjusting decision thresholds based on the confidence level for categorizing the input dataset as normal or including cyber-faults. The predicted fault nodes and the adjusted decision thresholds may be used for detecting cyber-faults in the plurality of nodes of the industrial assets.

A server-based environment for reporting a status of a security, monitoring and automation controller is provided. Detecting cessation of an always-on persistent network connection between the SMA controller and the server is also provided. Reporting the cessation of the network connection to an end user and defined others is further provided. A further aspect provides for automatically reporting an alarm event to a central station, the end user, and others, in the event the cessation of the network connection occurs while the SMA controller is armed and after a zone fault event, and not receiving a disarm notification prior to expiration of a preset entry delay.

Various systems described herein may allow a user's personal information, such as their medical history, to be automatically delivered to appropriate emergency personnel in response to a reported emergency, such as a 911 call, activation of a smoke alarm or security system, or other emergency. Various items of personal and confidential information may be stored in a secure location, and the user may provide advanced approval to have the information automatically delivered to emergency responders in response to a request for emergency assistance. For example, after the user initiates a request for emergency assistance, a computing device may determine whether the user has granted approval for emergency delivery of the user's confidential information, and if such approval has been granted, the computing device may request the delivery of the user's confidential information to the emergency responders.

Dispatch-aiding communications between computing devices of a responder and a dispatch unit include a computing device of the responder determining that an event occurred, automatically sending an indication of the event to a computing device of the dispatch unit, receiving a request for information from the computing device of the dispatch unit, obtaining the information requested by the computing device of the dispatch unit, and sending the information requested by the computing device of the dispatch unit to the computing device of the dispatch unit. The computing device of the dispatch unit sends the request for information to the computing device of the responder in response to receiving the indication of the event.

A method of vehicle accident response includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data. The method also includes analyzing the telematics data to determine that an accident involving the vehicle has occurred, analyzing the telematics data to determine a likelihood of injury due to the accident, and generating a communication (i) based upon the telematics data and the likelihood of injury and (ii) in response to determining that the accident has occurred. The method further includes transmitting, to the mobile device, the communication.

A method of vehicle accident response includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data. The method also includes analyzing the telematics data to determine that an accident involving the vehicle has occurred, analyzing the telematics data to determine a likelihood of injury due to the accident, and generating a communication (i) based upon the telematics data and the likelihood of injury and (ii) in response to determining that the accident has occurred. The method further includes transmitting, to the mobile device, the communication.