Computerized systems and methods for AI/ML determinations of user capabilities and fitness for military operations

A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a device, perform a method comprising steps of:

• receiving, by the device, a request from a requestor to determine a probability that a user is capable of performing a real-world task, the real-world task comprising a set of activities that are required to be completed for the real-world task to be considered completed;
• identifying, by the device, a user profile associated with the user, the user profile comprising characteristics of the user that indicate a skill-set of the user;
• analyzing, by the device, the user profile based on the real-world task, the analysis comprising inputting at least a portion of the characteristics of the user and information related to the set of activities into a predictive model to predict a performance of the user in each activity in the set of activities;
• determining, by the device, based on the analysis, an output indicating the predicted performance of the user in the real-world task, the output comprising information related to a status of the real-world task and a performance level associated with the status;
• transmitting, by the device, information related to the determined output to a device of a requestor;
• based on the output, generating, using one or more machine learning models, a treatment plan comprising at least a minimum range of motion;
• and
• automatically controlling, using the treatment plan, operation of at least one operating parameter at least one pedal of an electromechanical machine, wherein the operating parameter pertains to the at least minimum range of motion enabled by the at least one pedal and to a force on the at least one pedal, wherein the force is computable based on at least the at least minimum range of motion.

A device comprising:

• a processor configured to:
• receive a request from a requestor to determine a probability that a user is capable of performing a real-world task, the real-world task comprising a set of activities that are required to be completed for the real-world task to be considered completed;
• identify a user profile associated with the user, the user profile comprising characteristics of the user that indicate a skill-set of the user;
• analyze the user profile based on the real-world task, the analysis comprising inputting at least a portion of the characteristics of the user and information related to the set of activities into a predictive model to predict a performance of the user in each activity in the set of activities;
• determine, based on the analysis, an output indicating the predicted performance of the user in the real-world task, the output comprising information related to a status of the real-world task and a performance level associated with the status;
• transmit information related to the determined output to a device of a requestor;
• based on the output, generate, using one or more machine learning models, a treatment plan comprising at least a minimum range of motion;
• and
• automatically control, using the treatment plan, operation of at least one operating parameter of at least one pedal of an electromechanical machine, wherein the operating parameter pertains to the at least minimum range of motion enabled by the at least one pedal and to a force on the at least one pedal, wherein the force is computable based on at least the at least minimum range of motion.