Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success. The analyzing can include automatic identification of potential collision points for a first actor, including potential collision points with the newly discovered object. The newly discovered previously unmodeled entity interferes with an actor from performing an activity. The newly discovered object is a portion of a tool component of a product.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for analyzing ergonomic data from the one or more sensor streams.

In one embodiment a method comprises: accessing information associated with a first actor, including sensed activity information associated with an activity space; analyzing the activity information, including analyzing activity of the first actor with respect to a plurality of other actors; and forwarding feedback on the results of the analysis, wherein the results includes identification of a second actor as a replacement actor to replace the first actor, wherein the second actor is one of the plurality of other actors. The activity space can include an activity space associated with performance of a task. The analyzing can comprise: comparing information associated with activity of the first actor within the activity space with anticipated activity of the respective ones of the plurality of the actors within the activity space; and analyzing/comparing deviations between the activity of the first actor and the anticipated activity of the second actor.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing, restaurants and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for establishing traceability.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for process validation, anomaly detection and in-process quality assurance.

Workspace coordination systems and methods are presented. A method can comprise: accessing in real time respective information associated with a first actor and a second actor, including sensed activity information; analyzing the information, including analyzing activity of the first actor with respect to a second actor; and forwarding respective feedback based on the results of the analysis. The feedback can includes an individual objective specific to one of either the first actor or the second actor. The feedback includes collective objective with respect to the first actor or the second actor. The analyzing can include automated artificial intelligence analysis. Sensed activity information can be associated with a grid within the activity space. It is appreciated there can be various combinations of actors (e.g., human and device, device and device, human and human, etc.). The feedback can be a configuration layout suggestion. The feedback can be a suggested assignment of a type of actor to an activity.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of work charts.

Embodiments of the present invention provide a machine and continuous data set including process data, quality data, specific actor data, and ergonomic data (among others) to create more accurate job assignments that maximize efficiency, quality and worker safety. Using the data set, tasks may be assigned to actors based on objective statistical data such as skills, task requirements, ergonomics and time availability. Assigning tasks in this way can provide unique value for manufacturers who currently conduct similar analyses using only minimal observational data.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more senor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for contextual training using the one or more sensor streams and machine learning based action recognition.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for process validation, anomaly detection and in-process quality assurance.