Abstract: A sensing system includes a sensor assembly and a back end server system. The sensor assembly includes a collection of sensors in communication with a control circuit. The sensors are each configured to sense one or more physical phenomena in an environment of the sensor assembly. The control circuit of the sensor assembly is configured to identify one or more selected sensors of the collection of sensors whose data corresponds to an event occurring in the environment of the sensor assembly and transmit data to the back end server system. The back end server system is configured to generate a first order virtual sensor by training a machine learning model to detect the event based on the data from at least one of the selected sensors and detect the event using the trained first order virtual sensor and data from the selected sensors.

Abstract: A sensing system includes a sensor assembly that is communicably connected to a computer system, such as a server or a cloud computing system. The sensor assembly includes a plurality of sensors that sense a variety of different physical phenomena. The sensor assembly featurizes the raw sensor data and transmits the featurized data to the computer system. Through machine learning, the computer system then trains a classifier to serve as a virtual sensor for an event that is correlated to the data from one or more sensor streams within the featurized sensor data. The virtual sensor can then subscribe to the relevant sensor feeds from the sensor assembly and monitor for subsequent occurrences of the event. Higher order virtual sensors can receive the outputs from lower order virtual sensors to infer nonbinary details about the environment in which the sensor assemblies are located.

Abstract: A sensing system includes a sensor assembly that is communicably connected to a computer system, such as a server or a cloud computing system. The sensor assembly includes a plurality of sensors that sense a variety of different physical phenomena. The sensor assembly featurizes the raw sensor data and transmits the featurized data to the computer system. Through machine learning, the computer system then trains a classifier to serve as a virtual sensor for an event that is correlated to the data from one or more sensor streams within the featurized sensor data. The virtual sensor can then subscribe to the relevant sensor feeds from the sensor assembly and monitor for subsequent occurrences of the event. Higher order virtual sensors can receive the outputs from lower order virtual sensors to infer nonbinary details about the environment in which the sensor assemblies are located.