Emotion Intervention System Using Biometric Segmentation
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Solution Overview
Problem
Current emotion computing technologies lack effective methods for real-time emotion identification and personalized intervention, failing to provide tailored emotional support based on users' dynamic emotional states and physical conditions.
Innovation Solution
A method and system for emotion intervention that identifies user emotions through real-time biometric data analysis, recommending interventions such as media output, ambient atmosphere adjustments, diet, psychological consultations, or physiotherapy by constructing an emotion intervention repository using text similarity matching algorithms and biometric data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time biometric data analysis is implemented for emotion identification, then emotion recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments emotion identification into multiple independent modules: facial expression recognition, voice emotion analysis, and physiological signal processing. Each module processes specific biometric data types separately, then results are integrated to achieve comprehensive emotion recognition accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary emotion state library that maps biometric data patterns to standardized emotion states. This intermediary layer simplifies the complex relationship between raw biometric data and emotion identification, enabling accurate real-time recognition without requiring direct complex processing of all biometric parameters simultaneously.
2Reliability
If personalized intervention recommendations are provided based on physical state, then intervention effectiveness is improved, but data processing requirements increase
Solution Approach 1:
The system applies local quality by selecting and processing only the specific biometric parameters most relevant to each user's current physical state and emotion. Rather than processing all available data uniformly, the system dynamically adjusts which data points to prioritize based on individual user characteristics and current conditions, reducing overall data processing requirements while maintaining intervention effectiveness.
Solution Approach 2:
The patent implements preliminary action by pre-processing and categorizing biometric data during collection, organizing it into structured formats that facilitate efficient later analysis. User baseline data is also collected and stored in advance, enabling faster real-time comparison and recommendation generation without requiring intensive processing of raw data during intervention moments.
3Adaptability or versatility
If multiple intervention types are recommended (media, ambient, diet, therapy), then user support comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The system employs a universal intervention recommendation engine that handles multiple intervention types through a single integrated framework. The same core algorithm processes different data inputs and generates appropriate recommendations across media output, ambient atmosphere adjustment, diet suggestions, and therapy referrals, achieving comprehensive user support without requiring separate complex systems for each intervention type.
Data Source
AI summary
The present disclosure relates to a method, device, and system for emotion intervention, as well as a computer-readable storage medium and a healing room. The emotion intervention method includes: identifying a user's emotion state according to the user's first biometric information; and recommending at least one emotion intervention corresponding to the emotion state.