Systems, methods and apparatuses to detect an item left in a vehicle and to generate an alert about the item. For example, a camera configured in a vehicle can be used to monitor an item associated with a user of the vehicle. The item as in an image from the camera can be identified and recognized using an artificial neural network. In response to a determination that the item recognized in the image is left in the vehicle after the user has exited the vehicle, an alert is generated to indicate that an item is in the vehicle but the user is leaving the vehicle.

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 method and system for cross-verification of alarms in real-time comprising identifying primary variables and secondary variables causing the event, labelling the primary variables and the secondary variables by primary engine and secondary engine based on Artificial Intelligence based predictive model building, predicting the labels by one or more inference engine based on previous history and data patterns, triggering secondary engine for cross-verification of alarms whenever there is a prediction from the primary engine, identifying the correlation between the labels from the primary engine and the secondary engine by validation engine, identifying alarm type based on correlation, recommending predictive maintenance and displaying on dashboard the cross-verification status of the alarms. The method reduces misclassification of alarm types based on predictive or preventive maintenance, reduces the maintenance costs of assets, and helps in prioritizing the critical alarms based on the alert type.

There is provided a method and apparatus for detecting a fall event of the user. In particular, the method includes collecting data associated with activities of the user from a plurality of sensors and distributing the collected data to data sub-windows using signal windowing and segmentation, the data sub-windows indicative of a pre-fall moment, a fall moment, and a post-fall moment. The method further includes extracting a plurality of features from one or more of the data sub-windows and determining whether the event is a fall event at least in part based on the extracted features. The determination of whether the event is a fall event can further be determined by applied support vector machine (SVM) technique. The developed machine learning based methods may be substantially optimum in terms of a trade-off between accuracy and complexity of the evaluation. The method further includes multiple rejection filters in order to aid with the prevention of false alarms due to fall-like activities of daily living (ADLs). The method further includes a personalization process to update the machine learning based methods associated with each user.

According to some embodiments, disclosed are systems and methods for a novel framework of real-time event alert detection and communication. The disclosed framework operates by analyzing live-feeds of captured video at location and determining whether events lend towards a dangerous activity, then automatically alerting the users involved as to potential and/or imminent harm awaiting their actions. Rather than alerting one user, or a manger, as in conventional systems, the disclosed technology may evidence a communication relay among devices at a location, devices of users involved, as well as devices (and devices of users) overseeing operations within which the dangerous activity is anticipated or detected. This may lead to improved safety at and/or around workplace environments, as well as improved operational efficiency, thereby leading to reduced costs, reduced overhead and a reduction in resource expenditure.

A system and method for intelligently evaluating and automatically mitigating detected security activities includes implementing an on-premise security device that detects a potential security activity at a property of a subscriber; establishing a security channel between the on-premise security device and a remote machine learning-based security module operating in a cloud computing environment if the potential security activity satisfies escalation criteria; automatically transmitting, via the security channel, sensor data from the on-premise security device to the remote machine learning-based security module; computing, by the remote machine learning-based security module, a threat severity inference based on the sensor data; deriving device control instructions based on the threat severity inference; transmitting, via the security channel, the device control instructions to the on-premise security device; and mitigating the potential security activity by executing the device control instructions at the on-premise device.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to analyze data obtained from an access management device and other devices connected to the software platform. Other devices connected to the platform include robots, such as aerial robots, which are configured to detect motion and engage with an object connected to the motion detection. An enclosure is operable to house an aerial robot and provides for ease of addition of the aerial robot to a security or entry management system by providing an easily installable package. The enclosure provides the advantages of simple deployment and charging of aerial robots.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.