Emotion Detection Using User-Specific Baseline and Dominant Emotion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional emotion detection technologies lack user-specificity and fail to accurately track and improve emotional states, as they employ blanket rules that do not account for individual differences in emotional responses to situations.
Innovation Solution
An electronic apparatus that analyzes user-specific baseline emotions and dominant emotions over time, generating an emotional storyboard to recommend content or actions that improve emotional health by identifying emotional triggers and peaks, using a combination of biometric, audio, and image sensors, along with AI-driven analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If blanket emotion detection rules are used for all users, then the system complexity is reduced and ease of operation is improved, but measurement precision and reliability of emotion tracking deteriorate due to lack of user-specificity
Solution Approach 1:
The system segments emotion detection into two distinct components: user-specific baseline emotion detection and situation-specific dominant emotion detection. This segmentation allows the system to maintain simple operation while achieving precise tracking by separating the generalization aspect (baseline) from the specificization aspect (dominant emotion triggers).
Solution Approach 2:
The system performs preliminary action by detecting and establishing each user's baseline emotion profile before situation-specific emotion detection. This preliminary baseline detection enables the system to quickly identify dominant emotions by comparing current emotional states against the pre-established user-specific baseline, thereby achieving both ease of operation and measurement precision.
2Productivity
If continuous monitoring of user emotion is implemented, then productivity and responsiveness are improved, but loss of information increases due to lack of intelligent filtering and analysis capabilities
Solution Approach 1:
The system implements feedback by continuously monitoring user emotions and comparing real-time emotional states against the user's baseline profile. This feedback mechanism enables intelligent filtering that identifies only significant emotional deviations (dominant emotions) while discarding normal variations, thereby maintaining high productivity without information loss.
Solution Approach 2:
The system extracts only the most relevant information from continuous emotion monitoring data by identifying dominant emotions that significantly deviate from the user's baseline. This extraction process filters out redundant information while preserving critical emotional insights, achieving both continuous monitoring capability and information quality.
3Measurement precision
If user-specific baseline emotion detection is implemented, then measurement precision and reliability are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system performs preliminary action by establishing user-specific baseline emotion profiles during an initial detection phase. This preliminary baseline is stored and reused for subsequent emotion detection, eliminating the need to re-detect baseline emotions repeatedly and thereby reducing the complexity of ongoing emotion analysis while maintaining high measurement precision.
Solution Approach 2:
The system applies partial action by focusing emotion detection resources only on identifying dominant emotions that significantly deviate from the baseline, rather than analyzing every subtle emotional variation. This selective approach reduces measurement complexity while maintaining precision for emotionally significant events.
Data Source
AI summary
Electronic apparatus that stores user information received from a plurality of sensor that tracks user activities of a user over a specified time period. The electronic apparatus includes circuitry that detects a baseline emotion of the user based on the user information. The circuitry detects a dominant emotion of the user based on a change in an emotional characteristic of the user and the detected baseline emotion. The circuitry further recommends content and an emotional storyboard to the user based on specified emotion associated with the content, the baseline emotion, and the dominant emotion. The recommended content and the emotional storyboard is to induce a change in an emotional type of the dominant emotion from a negative emotional type to a positive emotional type.