Subliminal Facial Response Detection via Transdermal Optical Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting subliminal human responses are invasive, expensive, and not suitable for wide everyday usage, as they often require technical expertise and are prone to motion artifacts, making them inaccessible for practical applications such as market analytics.
Innovation Solution
A computer-implemented method and system using machine learning to detect subliminal facial responses by capturing and analyzing facial data from regions of interest with transdermal optical imaging, thermal imaging, and eye tracking, allowing for remote, non-invasive, and non-intrusive detection of subliminal stimuli responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional invasive techniques (polygraphs, EMG, EEG) are used to detect subliminal responses, then measurement precision is improved, but device complexity and ease of operation deteriorate due to requiring technical expertise and being invasive
Solution Approach 1:
The patent replaces complex mechanical and physiological sensing systems (polygraphs, EMG electrodes, EEG caps) with optical imaging systems that capture facial blood flow patterns. This substitution maintains measurement precision while dramatically reducing device complexity and eliminating the need for invasive sensor attachment.
Solution Approach 2:
The patent creates an optical copy of physiological information by capturing facial blood flow patterns through imaging. Instead of directly measuring physiological signals with complex sensors, the system captures visual representations of blood flow that can be analyzed to detect subliminal responses, simplifying the detection mechanism.
2Measurement precision
If hyperspectral imaging is used to capture blood flow changes, then measurement precision is improved, but device complexity and cost worsen due to storage and processing requirements
Solution Approach 1:
The patent extracts only the essential information needed for detection by focusing on specific wavelength ranges and facial regions that contain the most relevant blood flow signal. This extraction approach maintains measurement precision while reducing the volume of data that requires storage and processing.
Solution Approach 2:
The patent applies local quality by concentrating analysis on specific regions of interest on the face where blood flow changes are most indicative of subliminal responses. This localized approach improves signal-to-noise ratio while reducing the overall processing complexity compared to analyzing the entire facial surface with full hyperspectral data.
3Measurement precision
If conventional physiological sensors are attached to the face or body, then measurement precision is improved, but ease of operation deteriorates due to being invasive and intrusive
Solution Approach 1:
The patent replaces mechanical sensor attachment systems with non-contact optical imaging. The imaging system captures facial blood flow patterns without requiring any physical contact with the subject, thereby maintaining measurement precision while dramatically improving ease of operation and subject comfort.
Solution Approach 2:
The patent obtains physiological information through optical copying of facial blood flow patterns rather than through direct physical sensing. This copying approach eliminates the need for sensor attachment while preserving the ability to detect subliminal responses accurately.
4Measurement precision
If fMRI is used for detecting emotional responses, then measurement precision is improved, but device complexity and cost worsen to the point of being prohibitively expensive
Solution Approach 1:
The patent replaces complex fMRI imaging systems with simpler optical imaging technology. By substituting the sophisticated magnetic resonance imaging mechanism with optical blood flow detection, the system maintains the ability to detect emotional responses while reducing device complexity and cost to levels suitable for everyday use.
Solution Approach 2:
The patent captures physiological information related to emotional responses through optical imaging of facial blood flow patterns, creating a simplified copy of the information that fMRI would provide. This approach maintains measurement precision for emotional detection while eliminating the need for expensive and complex fMRI equipment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and cost-effective detection of subliminal facial responses to subliminal stimuli, providing probability measures and intensity analysis without the need for invasive sensors, suitable for various applications including market analytics and affective neuroscience.
Implementation Method 1
capturing and analyzing facial data from regions of interest with transdermal optical imaging
Implementation Method 2
capturing and analyzing facial data from regions of interest with transdermal optical imaging, thermal imaging
Data Source
AI summary
A system and method for detecting subliminal facial responses of a human subject to subliminal stimuli. The method includes: receiving captured first facial response data approximately time-locked with a presentation of subliminal target stimuli to a plurality of human subjects; receiving captured second facial response data approximately time-locked with a presentation of subliminal foil stimuli to the plurality of human subjects; receiving captured unidentified facial response data to a subliminal stimulus from the target human subject; determining a target probability measure that the unidentified facial response data of the target human subject is in response to the subliminal target stimuli using a machine learning model trained with a subliminal response training set, the subliminal response training set comprising the first captured facial response data and the captured second facial response data; and outputting the target probability measure.