Eye Tracking Training Template for Facial Emotion Recognition
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Solution Overview
Problem
Schizophrenia patients face difficulties in recognizing and interpreting facial expressions, which hampers their social interactions and empathy, and existing technologies lack effective solutions for improving these skills.
Innovation Solution
A training template construction apparatus using eye tracking data to generate personalized training templates through machine-learning algorithms, heat maps, and difference heat maps, guiding users to focus on specific facial regions for improved emotion recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional emotion recognition training methods are used, then training can be provided to schizophrenia patients, but the training effectiveness is insufficient and patients cannot accurately recognize facial expressions
Solution Approach 1:
The system implements feedback by analyzing the user's gaze pattern through eye tracking and comparing it with reference gaze patterns. The heat map and difference heat map provide visual feedback showing where the user is looking versus where they should be looking, enabling the user to adjust their gaze behavior to improve emotion recognition accuracy.
Solution Approach 2:
The system changes parameters by quantifying gaze behavior into measurable parameters such as gaze fixation duration, gaze movement paths, and heat map distributions. These parameters are analyzed and compared to generate training templates that systematically modify the user's gaze patterns to improve facial expression recognition.
2Loss of information
If eye tracking apparatus is used to capture gaze fixation points, then detailed gaze data can be obtained, but the system complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing computational algorithms that process raw eye tracking data into meaningful patterns. The gaze pattern extraction unit and heat map deduction unit act as intermediaries that transform complex raw gaze fixation points into simplified, actionable training information without requiring direct manual analysis.
Solution Approach 2:
The system implements self-service by automatically analyzing the collected gaze data and generating training templates without requiring external expert intervention. The machine-learning algorithms automatically extract gaze patterns, generate heat maps, and create personalized training programs, reducing the need for manual system configuration and expert analysis.
3Adaptability or versatility
If heat map and difference heat map analysis is performed, then personalized training templates can be generated, but the processing time and computational resources increase
Solution Approach 1:
The system applies preliminary action by pre-storing reference gaze patterns and heat maps for various emotions before actual training begins. This allows the system to quickly compare user gaze patterns against pre-computed references and generate training templates without performing computationally intensive analysis from scratch during each training session.
Data Source
AI summary
A training template construction apparatus includes a gaze fixation point receiving unit for receiving gaze fixation points of a user that looks a facial picture that expresses random emotion, from an eye tracking apparatus that is operatively associated with the gaze fixation point receiving unit, a gaze pattern extraction unit for extracting a gaze pattern and gaze pattern information via machine-learning of the gaze fixation points received from the gaze fixation point receiving unit, a heat map deduction unit for deducing a heat map using the gaze pattern and the gaze pattern information that are extracted by the gaze pattern extraction unit, a difference heat map deduction unit for deducing a difference value between the heat map deduced from the heat map deduction unit and a heat map of a reference group based on pre-stored facial pictures that express the same emotion and for deducing a difference heat map to which the difference value is applied, and a controller for analyzing the gaze pattern and the difference heat map to generate a training template of a sequence, a time, and a path for user gaze treatment.