WEARABLE MENTAL DISORDER AUTOMATIC DIAGNOSIS SYSTEM AND METHOD BASED ON CONTRASTIVE LEARNING

Abstract

Disclosed are a wearable mental disorder automatic diagnosis system and a method based on contrastive learning. The system is realized by a wearable device and includes a data acquisition unit for obtaining multi-modal physiological data of a user; a user registration unit for executing 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; training a personalized classifier using the data features as input to obtain a mental disorder recognition model; and a recognition unit for obtaining recognition results using the mental disorder recognition model when it is determined that the user is not a new user.

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