Emotion Detection Feedback for Video Conference Self-Awareness
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Solution Overview
Problem
Current video conference systems do not provide users with information about their own visualization and how they are perceived by others, lacking emotional state feedback which can impact communication effectiveness.
Innovation Solution
A computer-implemented method and system that determines a user's emotional state by obtaining their facial expression, analyzing it using known patterns and machine learning algorithms, and displaying the emotional state back to the user through a web browser, while keeping this information private from other participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If facial expression analysis is implemented to provide emotional state feedback, then communication effectiveness is improved, but device complexity increases
Solution Approach 1:
The system provides feedback to users about their own emotional states during video conferences by analyzing facial expressions and displaying the results. This feedback loop enables users to become aware of their non-verbal communication patterns and adjust their behavior accordingly, improving communication effectiveness.
Solution Approach 2:
The system uses an intermediary analysis service that processes facial expression data and translates it into emotional state information. This intermediary layer simplifies the complexity by abstracting the technical details of facial analysis into user-friendly emotional indicators that can be directly applied to communication improvement.
2Ease of operation
If emotional state monitoring is provided to users, then self-awareness is improved, but loss of information increases
Solution Approach 1:
The system extracts only the essential emotional state information from complex facial expression data and presents it in a simplified format to users. By taking out only the necessary emotional indicators rather than sharing complete facial analysis data, the system provides self-awareness while minimizing information loss and privacy concerns.
Solution Approach 2:
The system provides emotional state information locally to each user individually, allowing them to understand their own presentation without exposing sensitive information about other participants. This localized approach to information sharing enables self-awareness while preserving privacy.
3Productivity
If facial expression analysis is performed, then communication quality is improved, but measurement precision requirements increase
Solution Approach 1:
The system transforms complex facial expression parameters into simplified emotional state categories. By changing the representation from detailed facial geometry to standardized emotional indicators, the system achieves sufficient measurement precision for communication improvement while reducing the complexity of data processing and analysis.
Data Source
AI summary
A computer-implemented method for determining an emotional state of a user based on a facial expression of the user and causing to display the emotional state of the user to the user is provided.