Mental Load Index via Eye Feature Spectral Analysis
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Solution Overview
Problem
Current methods for determining mental load, such as eye tracking, face challenges in accurately capturing rapid changes in mental states due to noise and artifacts, and struggle to isolate task-related stress from external factors like sleep deprivation, leading to inaccurate and inefficient measurements.
Innovation Solution
A method that analyzes spatiotemporal fluctuations in eye features, calculates spectral patterns, and correlates these patterns with dynamic stimuli to determine a mental load index, allowing for real-time classification of mental states with high temporal resolution, using techniques like Fourier Transform and fuzzy c-mean clustering, and integrates feedback from physiological responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eye tracking is used to monitor eye motion, then mental load can be measured, but noise and artifacts interfere with accurate capture of rapid changes in mental states
Solution Approach 1:
The patent extracts and removes noise and artifacts from eye tracking data through signal processing techniques. Specifically, it separates task-related eye movement patterns from extraneous factors like sleep deprivation by applying filtering algorithms that isolate the relevant physiological signals associated with mental load during task performance.
Solution Approach 2:
The system uses feedback from physiological responses to continuously adjust and refine mental load measurements. By monitoring eye features in real-time and comparing them against baseline data and task difficulty levels, the system dynamically corrects for noise and artifacts, improving measurement accuracy over time.
2Measurement precision
If traditional eye tracking methods are used, then mental load can be assessed, but task-related stress cannot be isolated from external factors like sleep deprivation
Solution Approach 1:
The patent segments mental load measurement into task-related components and external factor components. By analyzing eye movement patterns specifically during task performance and comparing them against baseline measurements taken under controlled conditions, the system isolates the stress component directly related to task difficulty while separating out confounding factors like sleep deprivation.
Solution Approach 2:
The system performs preliminary calibration by establishing baseline eye movement characteristics before task performance. This preliminary action creates a reference profile that accounts for individual differences and external factors, allowing subsequent measurements to focus specifically on task-related stress by subtracting the baseline component.
3Measurement precision
If eye features are analyzed in real-time, then rapid changes in mental states can be captured, but measurement efficiency decreases due to complex processing
Solution Approach 1:
The patent applies partial action by selecting and analyzing only the most informative eye features rather than processing all possible eye movement data. It focuses on specific metrics such as pupil diameter changes, saccade frequency, and fixation duration that have the highest correlation with mental load, thereby maintaining high temporal resolution while reducing computational burden.
Solution Approach 2:
The system dynamically adjusts analysis parameters based on task phase and detected mental state. By changing the temporal resolution and feature extraction intensity according to the current measurement context, the system optimizes the balance between capturing rapid changes and maintaining processing efficiency throughout the measurement period.
Data Source
AI summary
The present invention provides methods and systems for determining mental states of a user based on mental load index, comprising: analysing image data to identify feature(s) of at least one eye while presenting user with dynamic physiological-responsive stimuli; calculating, based on the identified feature(s), spatiotemporal fluctuations over time for the feature(s); calculating, based on the spatiotemporal fluctuations, spectral pattern(s) having a first and second frequency; calculating mental load pattern(s) by bounding ratio between the spectral patterns for the first and second frequencies in time interval(s) corresponding to stimuli level changes; identifying correlation between each mental load pattern(s) and the stimuli level in the time interval(s); and determining, based on the correlation, a mental load index comprising time interval(s) corresponding to a plurality of mental states identified by comparing each mental load pattern(s) to a threshold; and classifying mental state(s) based on the mental load index in each time interval(s).