A method for monitoring devices based at least in part on detected conditions includes accumulating, by one or more sensory nodes, sensed information in an area that includes a controllable device. The method also includes analyzing the sensed information to identify historical information regarding the area that includes the controllable device. The method also includes sensing a condition within the area by the one or more sensory nodes. The method also includes determining, based at least in part on the sensed condition and at least in part on the historical information, that the sensed condition relates to the controllable device. The method further includes generating, responsive to said determining, an alert regarding the controllable device.

Embodiments propose methods and system for predicting the occurrence of critical alarms in response to the occurrence of less severe, non-critical alarms. It is proposed to use a machine-learning model trained to discern whether a non-critical alarm will be followed by a critical alarm within a particular time period, e.g. whether the non-critical alarm will develop into a critical alarm. Unlike existing alarm systems which are merely threshold based, this approach uses physiological data from a window of data. This window of data can be expected to carry more information than a simple breach of the threshold.

Embodiments propose methods and system for predicting the occurrence of critical alarms in response to the occurrence of less severe, non-critical alarms. It is proposed to use a machine-learning model trained to discern whether a non-critical alarm will be followed by a critical alarm within a particular time period, e.g. whether the non-critical alarm will develop into a critical alarm. Unlike existing alarm systems which are merely threshold based, this approach uses physiological data from a window of data. This window of data can be expected to carry more information than a simple breach of the threshold.

A computer implemented method including receiving, by a monitoring system that is configured to monitor a property and from an electronic pool device that is configured to monitor a swimming pool at the property, sensor data, analyzing, by the monitoring system, the sensor data, based on analyzing the sensor data, generating, by the monitoring system, an instruction to activate a camera of the electronic pool device, providing, by the monitoring system to the electronic pool device, the instruction to activate the camera, receiving, by the monitoring system from the electronic pool device, image data, analyzing, by the monitoring system, the image data, based on analyzing the image data, identifying a monitoring system action to perform, and performing the monitoring system action.

Systems and methods are provided for improved pedestrian and vehicle safety along with systems and methods for obtaining or sharing data from surrounding mobile assets in the case of an occurrence of an event of interest, an impending event of interest or emergency situation.

Systems and methods for detecting potential theft and identifying individuals having a history of committing theft are presented. An electromagnetic emission associated with a personal electronic device associated with an individual is received. One or more signal properties of the electromagnetic emission are analyzed to determine an emission signature. Video data and video analytics are utilized to determine whether an individual has taken possession of an item. The video analytics are correlated with the emission signature in an attempt to identify the individual having possession of the item. The emission signature and video data are stored for later use during a checkout procedure. If an emission signature detected at a checkout station matches that of the individual having possession of the item, and the item is not processed through the checkout station, an alert is issued and the individual is flagged as a potential shoplifter.

Systems and methods for detecting potential theft and identifying individuals having a history of committing theft are presented. An electromagnetic emission associated with a personal electronic device associated with an individual is received. One or more signal properties of the electromagnetic emission are analyzed to determine an emission signature. Video data and video analytics are utilized to determine whether an individual has taken possession of an item. The video analytics are correlated with the emission signature in an attempt to identify the individual having possession of the item. The emission signature and video data are stored for later use during a checkout procedure. If an emission signature detected at a checkout station matches that of the individual having possession of the item, and the item is not processed through the checkout station, an alert is issued and the individual is flagged as a potential shoplifter.

A method for use in a lighting system for rendering emergency lighting comprises detecting (101) a switch from a normal mode of the lighting system to an emergency mode of the lighting system and a switch from the emergency mode to the normal mode and activating (103) an emergency radio frequency-based sensing function upon detecting the switch from the normal mode to the emergency mode. The radio frequency-based sensing comprises detection of disturbances to a radio frequency field. The method further comprises deactivating (105) the emergency radio frequency-based sensing function upon detecting the switch from the emergency mode to the normal mode.

A method for use in a lighting system for rendering emergency lighting comprises detecting (101) a switch from a normal mode of the lighting system to an emergency mode of the lighting system and a switch from the emergency mode to the normal mode and activating (103) an emergency radio frequency-based sensing function upon detecting the switch from the normal mode to the emergency mode. The radio frequency-based sensing comprises detection of disturbances to a radio frequency field. The method further comprises deactivating (105) the emergency radio frequency-based sensing function upon detecting the switch from the emergency mode to the normal mode.

A handheld monitoring and early warning device for Fusarium head blight of in-field wheat includes an acquisition card, a processor, a camera, a touchscreen, a power supply, and a 4G network card. The acquisition card is configured to acquire data. The processor is configured to analyze the acquired data, to obtain the growth of wheat based on a deep learning algorithm. The camera is configured to acquire root, stem, and ear information of in-field wheat. The touchscreen is a medium configured to perform human-computer interaction. The power supply is configured to supply power to the monitoring and early warning device. The 4G network card is configured to perform data communication and at the same time communicate with an external cloud server. Further disclosed is an early warning method of a handheld monitoring and early warning device for Fusarium head blight of in-field wheat.