Depression Response Evaluation Using Emotion-Text Consistency
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Solution Overview
Problem
Conventional diagnostic questionnaires for depression are limited by subjective reporting, leading to unreliable assessments due to patients' memory, understanding, and honesty in answering questions.
Innovation Solution
A detection system and method that utilizes voice, image, and physiological data analysis to determine the authenticity of responses by comparing emotional states with speech content, facial expressions, eye movements, and heart rate data, employing machine learning algorithms for comprehensive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional diagnostic questionnaires are used for depression assessment, then the assessment process is simple and quick, but the reliability of the assessment results deteriorates due to subjective reporting limitations
Solution Approach 1:
The assessment system segments the evaluation into multiple independent components: voice analysis (tone, pitch, rhythm), image analysis (facial expressions, eye movements), and physiological data (heart rate). Each component independently evaluates emotional state, and their results are integrated to form a comprehensive assessment, thereby improving reliability while maintaining efficiency
Solution Approach 2:
The system introduces an intermediary AI analysis module that objectively processes and compares the tested person's responses with their actual emotional states detected through voice, image, and physiological sensors. This intermediary layer eliminates subjective reporting biases and provides reliable validation of response authenticity
2Reliability
If multiple data types (voice, image, physiological) are collected and analyzed, then the assessment reliability improves, but the device complexity increases
Solution Approach 1:
The terminal device is designed with multi-functionality, serving both as a communication device and as a comprehensive data collection platform. The same device captures voice information, images, and physiological data, and runs the AI analysis algorithms, eliminating the need for separate specialized equipment and reducing overall system complexity
Solution Approach 2:
The system merges multiple data collection functions (voice recording, image capture, physiological sensing) and analysis functions (voice analysis, facial expression recognition, heart rate monitoring) into a single integrated AI analysis module. This consolidation simplifies the system architecture while maintaining the ability to process multiple data types for reliable assessment
Data Source
AI summary
A detection system, comprising an interaction module, a receiving module, and an analysis module. The interaction module is configured to interact with a tested person, and the interaction module includes an audio acquisition unit to collect voice information emitted by the tested person. The receiving module is electrically connected to the interaction module to generate sound frequency data and speech text data based on the voice information obtained by the audio acquisition unit, and the analysis module is electrically connected to the receiving module. When the tested person responds to at least one question posed by the interaction module, causing the interaction module to generate voice information, the analysis module determines the emotional state of the tested person based on the sound frequency data, and assesses whether the tested person's response aligns with their emotional state based on the speech text data. If the tested person's response aligns with their emotional state, the response is judged as truthful; otherwise, it is judged as false.