Wearable mental disorder automatic diagnosis system and method based on contrastive learning

A wearable mental disorder automatic diagnosis system based on contrastive learning, wherein the system is realized by adopting a wearable device and comprises:

• a data acquisition unit configured to obtain multi-modal physiological data of a user;
• a user registration unit configured to execute the following steps under the condition that the user is determined to be a new user:
• fine-tuning a first feature encoder which is pre-trained offline by using the multi-mode physiological data in a self-supervised contrastive learning mode to obtain a second feature encoder;
• extracting data features from labeled multi-modal physiological data through a second feature encoder;
• and training a personalized classifier using the data features as input to obtain a mental disorder recognition model, which is constructed based on the second feature encoder and the trained personalized classifier;
• and
• a recognition unit configured to obtain recognition results using the mental disorder recognition model when the user is not determined to be a new user.