GMECI, LLC

Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.

Aspects relate to systems and methods for inspirate sensing to determine a probability of an emergent physiological state. An exemplary system an inhalation sensor module configured to sense and transmit a plurality of inhalation parameters as a function of at least an inspirate, an environmental sensor module configured to sense and transmit a plurality of environmental parameters as a function of an environment, and a processor configured to generate a probability of an emergent physiological state by: inputting at least an environmental parameter and at least an inhalation parameter to a probabilistic machine learning model and generating the probability of an emergent physiological state as a function of the machine learning model.

Aspects relate to respiration system and a method of use. An exemplary system includes a face mask configured to substantially seal a chamber about a nose and mouth of a user, an exhalation system in fluidic communication with the face mask and configured to permit flow of expirate from the user, wherein the exhalation system additionally includes a valve selectively permitting fluidic communication with the chamber as a function of chamber pressure and an actuator operatively connected to the valve, a respiration sensor configured to detect a respiration parameter associated with a respiration phenomenon, and a computing device in communication with the actuator and the respiration sensor, wherein the computing device is configured to receive the respiration parameter and control the actuator.

A device for detecting physiological parameters is used to detect a degree of user hypoxemia in response to flight conditions; degree of hypoxemia may be used automatically to modify actions by a training device such as a reduced oxygen breathing device or centrifuge and generate feedback or modifications to training profiles for future use. Machine learning processes may be combined with sensor activity to discover relationships between maneuvers, environmental conditions, physiological parameters, and degrees of impairment to develop optimal flight or training plans.