Eye Motion Data Correlation for User Activity Detection
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Solution Overview
Problem
Current eye-tracking technologies for human visual behavior analysis are inefficient and inaccurate due to the need for manual classification and management of short-term visual activity data, limiting their effectiveness in understanding user behavior.
Innovation Solution
An information processing method and electronic device that acquire eye motion data, extract eyeball position data, and establish a correspondence relationship between eye motion and user behavior activity data, enabling automatic determination of current user behavior through encoding and bag-of-words analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual classification and management of eye tracking data is used, then the system can process visual activity information, but the efficiency and accuracy of user activity judgment is low
Solution Approach 1:
The system automatically correlates eye motion data with user behavior activity data through time-based correspondence relationships, eliminating the need for manual classification and management of eye tracking data. The processor autonomously determines user behavior activities by comparing acquisition times and establishing correlations between eye position data and behavior activity data.
Solution Approach 2:
The patent replaces manual mechanical classification processes with an automated computational system that uses time-stamped data correlation algorithms. The processor automatically matches eye motion data with behavior activity data based on acquisition time, substituting human manual analysis with machine-based automated determination of user behavior activities.
2Measurement precision
If short-term eye tracking data is collected, then visual activity information can be obtained, but the accuracy of user behavior analysis is insufficient
Solution Approach 1:
The system performs preliminary correlation analysis by establishing time-based correspondence relationships between eye motion data and user behavior activity data before making behavior determinations. By pre-processing the data to create temporal associations, the system improves the accuracy of user behavior analysis while working effectively with short-term collected data.
Solution Approach 2:
The system uses feedback from the correspondence relationship between eye motion data and user behavior activity data to continuously improve behavior analysis accuracy. By comparing acquired eye position data with recorded behavior activity data over time, the system refines its determination of current user behavior activities, enhancing measurement precision through iterative feedback.
Data Source
AI summary
An information processing method is provided. The information processing method includes acquiring a motion state of eyes of a user to form eye motion data and record a first acquisition time of the eye motion data; extracting position data of the user's eyeballs from the eye motion data; and capturing user behavior activity data to record a second acquisition time of the user behavior activity data. The method also includes, based on the first acquisition time and the second acquisition time, determining a correspondence relationship between the position data of the user's eyeballs and the user behavior activity data; and, based on the correspondence relationship and a current eye motion, determining a current user behavior activity.