Systems and methods for identifying breathing comfort

A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for training a machine-learning model for classifying a user of a respiratory pressure therapy (RPT) device, the method comprising:

• receiving input data associated with users and associated therapy sessions, wherein each user has one or more classifications;
• processing the input data to extract one or more features from the input data;
• training the machine learning model to classify one or more other users into the one or more classifications, based on the one or more feature extracted from the input data;
• and
• providing communication between the machine learning model and one or both of a user device and the RPT device, wherein the communication comprises at least causing a user device and/or the RPT device to execute an action.

A system for classifying a user of a respiratory pressure therapy (RPT) device, comprising:

• a memory storing instructions and a machine learning model trained based on pre-classified users so as to learn associations between the pre-classified users and a plurality of classifications, such that the machine learning model is configured to perform the classifying based on the learned associations;
• and
• a processor operatively connected to the memory and configured to execute the instructions to perform operations including:
• identifying that the user has commenced a therapy session on the RPT device;
• receiving first input data associated with the user and the therapy session;
• classifying the user into a classification of the plurality of classifications by inputting the first input data into the machine learning model;
• and
• in response to classifying the user into the classification, causing the user device and/or the RPT device to execute an action.