Personalized Emotion Detection Using Audio Video Calibration
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Solution Overview
Problem
Current emotion detection technologies face challenges in accurately recognizing individual emotional states due to the individuality and cultural variations in emotional expression, often relying on large datasets and multiple sensors, which can lead to imprecise analysis and require extensive calibration.
Innovation Solution
A detection device and system that uses a processing unit to analyze both audio and video data, with calibration sets of user-specific data to accurately recognize emotions, employing a deep neural network for precise emotion detection and providing a user-friendly calibration process, including instructions for recording and tagging emotional states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets and multiple sensors are used for emotion detection, then the system can capture more data for analysis, but the device complexity and data processing requirements increase significantly
Solution Approach 1:
The system segments the emotion detection task into distinct modules: audio feature extraction, visual feature extraction, and emotional state classification. Each module processes specific types of data independently before integrating results, reducing overall system complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The system performs preliminary calibration with the user to establish baseline emotional states and individual expression patterns. This pre-processing step enables the system to achieve higher precision with fewer sensors and less data, as the calibration phase captures individuality before actual emotion detection occurs
2Measurement precision
If multiple sensors are used to analyze emotions, then the accuracy of emotion detection improves, but the calibration process becomes more complex and time-consuming
Solution Approach 1:
The calibration process is designed to be self-guided through an interactive interface that automatically collects user responses and adjusts the model parameters. The system self-calibrates by presenting the user with emotional scenarios and automatically learning individual expression patterns, eliminating the need for manual calibration configuration
Solution Approach 2:
The system dynamically adjusts the weightings and thresholds of different sensor parameters during calibration based on user responses. By adapting the parameter settings to match individual user behavior patterns, the system achieves high accuracy with reduced calibration time compared to fixed-parameter approaches
3Adaptability or versatility
If self-supervised learning structures are trained on large databases, then general emotion recognition capability improves, but individual emotion analysis becomes imprecise due to lack of personalization
Solution Approach 1:
The system transitions from a static, general-purpose emotion model to a dynamic, personalized model that adapts to each user's unique expression patterns. The calibration phase collects individual data and continuously refines the model parameters, enabling the system to maintain general capability while achieving high precision for each specific user
Solution Approach 2:
The system applies different processing strategies for different users based on their individual characteristics captured during calibration. Each user receives a customized analysis model that is optimized for their specific expression patterns, allowing the system to handle both general emotion recognition and individualized analysis with appropriate precision for each context
4Productivity
If raw data from sensors is used directly, then the data processing steps are reduced, but the quality of data is poor with background noises and unclear voices
Solution Approach 1:
The system performs preliminary signal processing during the calibration phase to establish baseline noise profiles and characteristic patterns for each user. This pre-characterization enables the system to efficiently filter and clean raw data during actual emotion detection without requiring complex real-time processing
Solution Approach 2:
The system introduces an intermediary processing layer that combines raw sensor data with calibration-derived models to produce cleaned, quality-assured data. This intermediate representation integrates the simplicity of raw data processing with the quality improvement of sophisticated filtering, achieving both efficiency and precision
Data Source
AI summary
The invention relates to a detection device (10) for detecting the emotional state of a user. The detection device (10) comprises a processing unit (1) for processing data, in particular input data, a main data storage unit (2) for storing data, in particular input data and/or data processed by said processing unit and a connecting element (3a, 3b) for connecting the detection device (10) to an interface device (50), in particular a mobile phone or a tablet, and/or a recording device (7a, 7b). The detection device (10) is adapted to be calibrated to said user by use of the processing unit (1) and calibration data. In particular, the calibration data is at least one set, preferably five sets, of audio and video data of said user. The processing unit (1) is adapted to analyse input data based on said calibration. In particular, the processing unit (1) is adapted to compare input data to calibration data and to calculate the nearest approximation of the input data and the calibration data.