Wearable Mental Disorder Diagnosis Using Multimodal Contrastive Learning
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Solution Overview
Problem
Current mental disorder recognition technologies face challenges such as privacy breaches, high costs, low real-time performance, and difficulty in recognizing multiple mental disorders due to single-modality data and lack of multi-modal fusion, requiring large amounts of labeled data and lacking user personalization.
Innovation Solution
A wearable mental disorder diagnosis system using contrastive learning that collects multi-modal physiological data through ubiquitous devices, pre-trains a feature encoder with unlabeled data, and fine-tunes it with a small amount of labeled data to create personalized classifiers for real-time mental disorder recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recognition methods using questionnaires or specialized medical equipment are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts and utilizes superficial physiological information (heart rate variability, blood oxygen saturation) that can be obtained from ubiquitous wearable devices, separating the recognition task from specialized medical equipment. This extraction approach maintains measurement precision while eliminating the need for complex specialized equipment.
Solution Approach 2:
The patent uses superficial physiological information as a copy or proxy for deep physiological information that would require specialized equipment. By training contrastive learning models on readily available superficial data, the system achieves accurate mental disorder recognition without needing expensive medical devices.
2Ease of operation
If mobile sensing technology is used to collect user behavior information, then ease of operation is improved, but real-time performance deteriorates
Solution Approach 1:
The patent implements continuous collection and processing of superficial physiological information through wearable devices, enabling real-time monitoring of mental state. The contrastive learning model processes data continuously rather than in batches, maintaining both ease of operation and real-time performance.
Solution Approach 2:
The patent replaces traditional mechanical behavior observation methods with physiological signal processing. By using automated sensing of heart rate variability and blood oxygen saturation, the system achieves real-time detection without manual intervention, substituting mechanical data collection with automated physiological monitoring.
3Ease of manufacture
If self-supervised learning uses single modal signals for pre-training, then ease of manufacture is improved, but measurement precision deteriorates
Solution Approach 1:
The patent merges multiple modalities of superficial physiological information (heart rate variability, blood oxygen saturation, and other wearable device data) into a unified contrastive learning framework. This multi-modal fusion maintains ease of implementation while significantly improving recognition accuracy by capturing complementary information from different physiological sources.
Solution Approach 2:
The patent creates a composite representation by combining features from multiple physiological modalities. The contrastive learning model processes heterogeneous data types together, creating a rich feature space that improves recognition precision while maintaining the simplicity of self-supervised learning approaches.
4Measurement precision
If contrastive learning focuses on one disease, then measurement precision for that disease is improved, but adaptability deteriorates
Solution Approach 1:
The patent designs a universal contrastive learning framework that can recognize multiple types of mental disorders (depressive disorders, anxiety disorders, mood and affective disorders) using the same model architecture and training approach. The system processes multi-label classification tasks, enabling a single model to adapt to various diseases without requiring disease-specific customization.
Data Source
AI summary
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.