System and method for adaptive fusion of data from multiple sensors using context-switching algorithm

A computer program product for adaptive fusion of sensor data from multiple sensors having a plurality of non-transitory computer readable instructions, the plurality of non-transitory computer readable instructions arranged to be stored and executed on a memory and a processor, wherein the plurality of non-transitory computer readable instructions are operative to cause the processor to:

• collect sensor data generated by a plurality of sensors in an environment;
• determine a quality metric of the sensor data, using a data quality module 26, generated by each sensor of the plurality of sensors, the quality metric corresponding to a suitability of the sensor data for accomplishing a given task;
• select, via an assessment artificial intelligence program, one or more sensors from the plurality of sensors based on the determined quality metric of the sensor data that yields a desired accuracy of accomplishing the given task;
• and select for each sensor of the plurality of sensors selected, via the assessment artificial intelligence program, a machine learning algorithm from a predetermined set of machine learning algorithms, based on the determined quality metric of the sensor data, that yields the desired accuracy for accomplishing the given task;
• wherein the processor is further configured to:
• receive a classification output from each selected machine learning algorithm for each selected sensor, wherein the classification output is a result generated by accomplishing the task;
• and
• combine classification outputs using a decision-level fusion rule;
• wherein the decision-level fusion rule defines how the classification outputs are aggregated to arrive at a final classification to accomplish the given task.

A system for adaptive fusion of sensor data from multiple sensors, the system comprising:

• one or more tunable luminaires arranged to illuminate an environment;
• a controller having a processor, the processor arranged to:
• collect sensor data generated by a plurality of sensors in an environment;
• determine a quality metric of the sensor data generated by each sensor of the plurality of sensors, the quality metric corresponding to a suitability of the sensor data for executing a given task;
• select, via an assessment artificial intelligence program, one or more sensors from the plurality of sensors based on the determined quality metric of the sensor data that yields a desired accuracy for executing the given task;
• and
• select for each sensor of the plurality of sensors selected, via the assessment artificial intelligence program, a machine learning algorithm from a predetermined set of machine learning algorithms based on the determined quality metric of the sensor data that yields the desired accuracy for executing the given task;
• wherein the processor is further configured to:
• receive a classification output from each selected machine learning algorithm for each selected sensor, wherein the classification output is a result generated by executing the task;
• and
• combine classification outputs using a decision-level fusion rule;
• wherein the decision-level fusion rule defines how the classification outputs are aggregated to arrive at a final classification to accomplish the given task.