Methods and apparatus for predicting and preventing autistic behaviors with learning and AI algorithms

A system for predicting and responding to a subject's physiological state using a machine learning (ML) model, comprising:

• a plurality of sensors configured to collect at least one of physiological data and biometric data from the subject, the physiological data including at least one of heart rate, respiratory rate, temperature, heart rhythm, respiratory rhythm, audio measurements, muscle activity, oxygen levels, EEG, EKG, and motion data;
• at least one processor coupled to a memory storing executable instructions, wherein the processor is configured to:
• operate in a learning mode that includes configuring the processor to:
• train the ML model using a training dataset comprising a first subset of the at least one of the physiological data and the biometric data, wherein the training includes:
• labeling physiological data points where the subject exhibits particular physiological states and associating these data points with corresponding stimuli;
• optimizing ML model parameters by minimizing a loss function between predicted outputs and actual outputs using at least one of:
• gradient descent, backpropagation, or adaptive optimization algorithms;
• operate in an operational mode that includes configuring the processor to:
• input a second subset of the physiological data into the trained model, wherein the trained ML model processes the second subset of the physiological data to detect patterns of behavior and classify the subject's physiological state;
• predict that a current physiological state of the subject will transition to a future physiological state using the trained ML model based on the classification and a relationship between patterns of behavior at different times;
• generate an output signal corresponding to a stimulus that modifies the subject's physiological state to avoid the predicted transition to the future physiological state, wherein the stimulus is selected from stimuli including auditory signals, vibrations, visual stimuli, haptic feedback, or electrical stimulation;
• operate in a closed-loop feedback control mode during the operational mode that includes configuring the processor to:
• receive updated physiological data from the plurality of sensors after delivering a stimulus to measure a physiological response of the subject;
• determine whether the measured physiological response indicates improvement in the subject's physiological state toward a baseline range for the subject;
• in response to determining that the measured physiological response indicates improvement, maintain stimulus parameters while the subject's physiological state continues to improve;
• in response to determining that the measured physiological response does not indicate improvement, update the ML model parameters based on the measured physiological response and the delivered stimulus, generate an output signal corresponding to a different stimulus based on the updated ML model for delivering the different stimulus to the subject;
• and
• iterate the closed-loop feedback control mode during the operational mode such that stimulus selection is refined based on the measured physiological responses of the subject;
• and
• at least one stimulus device configured to receive the output signal and deliver the stimulus based on the output signal.