Pulsed Light Emotion Detection via Temporal Signal Segmentation
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Solution Overview
Problem
Current systems fail to accurately and efficiently estimate a user's psychological state by distinguishing between facial blood flow and cerebral blood flow changes, which are crucial for emotion detection and mental health monitoring.
Innovation Solution
A system comprising a light source that emits pulsed light, a photodetector to detect reflection light, and electrical circuitry that processes signals to differentiate between surface reflection and internal scattering components, using an emotion model to classify emotions based on intensity changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If contactless measurement methods are used to detect facial blood flow patterns, then non-invasive emotion detection is achieved, but measurement precision is insufficient to distinguish between facial and cerebral blood flow changes
Solution Approach 1:
The patent segments the reflected light signal into distinct temporal components: a first signal corresponding to surface reflection from the face and a second signal corresponding to internal scattering from the brain. By separating these signals in the time domain, the system achieves precise differentiation between facial and cerebral blood flow changes while maintaining non-invasive contactless measurement.
Solution Approach 2:
The patent adds the temporal dimension to the measurement by analyzing the time characteristics of reflected light signals. The falling period of the light pulse is divided into specific time intervals (first time interval for surface reflection, second time interval for internal scattering), enabling differentiation between facial and cerebral blood flow that cannot be achieved with simple intensity measurement alone.
2Device complexity
If simple light intensity detection is used, then device complexity is reduced, but emotion detection accuracy deteriorates due to inability to distinguish blood flow sources
Solution Approach 1:
The patent segments the detection process into two independent signal acquisition paths: one for surface reflection (first signal) and one for internal scattering (second signal). This segmentation allows the system to maintain relatively simple individual detection components while achieving high emotion detection accuracy through the differentiated information from both signals.
Solution Approach 2:
The patent enhances the detection capability by introducing temporal dimension analysis. Instead of relying solely on spatial or intensity-based differentiation, the system uses time-resolved detection during the falling period of the light pulse to distinguish between surface and internal reflections, thereby improving accuracy without requiring overly complex device architecture.
3Measurement precision
If comprehensive signal processing is applied to differentiate blood flow components, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent segments the signal processing into two independent parallel paths: processing the first signal for facial blood flow and processing the second signal for cerebral blood flow. This segmentation enables simultaneous extraction of both blood flow components without sequential processing delays, maintaining measurement precision while minimizing processing time.
Solution Approach 2:
The patent utilizes the temporal dimension during the falling period of the light pulse to achieve differentiation. By defining specific time intervals (first time interval for surface reflection, second time interval for internal scattering), the system performs differentiation in parallel within the same measurement cycle, reducing processing time compared to sequential analysis methods.
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 estimation of user emotions by distinguishing between facial and cerebral blood flow changes, improving emotion detection and mental health monitoring through precise signal processing and classification.
Implementation Method 1
a photodetector that detects at least part of reflection pulsed light that returns from the head portion
Implementation Method 2
a photodetector that detects at least part of reflection pulsed light that returns from the head portion and that outputs one or more signals corresponding to an intensity of the at least part of the reflection pulsed light
Implementation Method 3
the pattern of the facial blood flow changes according to the emotion of a target person... near-infrared spectroscopy is used to determine a major depressive disorder from the state of cerebral blood flow
Data Source
AI summary
A system includes: a light source that emits pulsed light that illuminates a user's head portion; a photodetector that detects at least part of pulsed light returning from the head portion and that outputs one or more signals corresponding to an intensity of the at least part; electrical circuitry; and a memory that stores an emotion model indicating a relationship between the one or more signals and emotions. Based on a change in the one or more signals, the electrical circuitry selects an emotion by referring to the model. The one or more signals include a first signal corresponding to an intensity of first part of the reflection pulsed light and a second signal corresponding to an intensity of second part of the reflection pulsed light. The first part incudes part before a falling period is started; and the second part includes at least part in the falling period.