Blink Rate Variability Signal Analysis for Sustained Visual Attention
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Solution Overview
Problem
Conventional methods for assessing visual sustained attention are limited in determining appropriate frequency regions of blink rate variability (BRV) signals indicative of visual attention, and they require expert intervention for scoring and interpretation.
Innovation Solution
A system and method that records gaze data using an eye tracker, reconstructs uniformly sampled BRV series signals, extracts time and frequency domain features, including the Pareto frequency feature, and compares these features with threshold values to determine sustained visual attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods for assessing visual sustained attention are used, then expert intervention is required for scoring and interpretation, but this increases device complexity and reduces productivity
Solution Approach 1:
The system automatically extracts features from BRV signals and compares them with pre-stored threshold values to determine attention levels, enabling the system to assess attention independently without expert intervention. The processor autonomously performs feature extraction, threshold comparison, and attention determination.
Solution Approach 2:
The patent replaces the manual expert assessment process with an automated computational system that uses signal processing algorithms to extract features from BRV signals and automatically determine attention levels based on threshold comparisons.
2Measurement precision
If conventional methods for determining frequency regions of BRV signals are used, then the assessment lacks precision, but improving measurement precision requires more complex analysis methods
Solution Approach 1:
The system extracts specific frequency domain features from BRV signals and compares them against pre-determined threshold values to identify attention-related frequency regions. This approach transforms the complex problem of frequency region determination into a parameter comparison task.
Solution Approach 2:
The patent replaces subjective expert judgment about frequency regions with an automated system that uses signal processing to extract frequency domain features and objectively compares them with threshold values for precise determination.
3Measurement precision
If detailed feature extraction and threshold comparison methods are implemented, then assessment accuracy improves, but the processing time increases
Solution Approach 1:
The system uses pre-stored threshold values for frequency domain features that have been determined in advance through preliminary analysis. During actual assessment, the system only needs to extract features and compare them with these pre-established thresholds, significantly reducing processing time while maintaining accuracy.
Data Source
AI summary
This disclosure relates generally to a method and system for assessment of sustained visual attention of a target. The conventional methods utilize various markers for assessment of attention, however, blink rate variability (BRV) series signal is not explored yet. In an embodiment, the disclosed method utilizes BRV series signal for assessing sustained visual attention of a target. A gaze data of the target is recorded using an eye tracker and a set of uniformly sampled BRV series signal is reconstructed from each of the BRV series. One or more frequency domain features, including pareto frequency, are extracted from the set of uniformly sampled BRV series signal. The values of frequency domain features extracted from the set of BRV series signals are compared with corresponding threshold values to determine visual sustained attention of the target.