Apparatus and method for estimating bio-information
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Solution Overview
Problem
Existing non-invasive bio-information estimation methods lack accuracy in determining parameters like triglycerides, body fat percentage, body water, blood glucose, cholesterol, carotenoid, and uric acid due to insufficient consideration of contact area and direction, leading to suboptimal sensor configuration and signal processing.
Innovation Solution
An apparatus and method that utilizes a sensor part with a pixel array of pixels, each equipped with a light source and detector, and a processor to adaptively determine a second sensor configuration based on contact information, such as contact area, direction, and fingerprint data, to enhance accuracy by optimizing light source and detector pixel combinations for improved signal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed sensor configuration is used for bio-information estimation, then the device structure is simple, but the measurement precision is insufficient due to inability to adapt to different contact conditions
Solution Approach 1:
The patent implements dynamic sensor configuration by allowing the system to automatically adjust which light sources and detectors are activated based on real-time contact detection. The contact detection unit identifies contact regions, and the control unit dynamically configures the sensor array to use only detectors within these contact regions, making the sensor system adaptive rather than static.
Solution Approach 2:
The system changes operational parameters (which specific light sources and detectors are active) based on contact conditions. By modifying the active sensor subset according to detected contact regions, the system optimizes measurement precision without requiring a physically complex reconfigurable sensor array.
2Measurement precision
If all pixels in the sensor array are activated, then complete coverage is achieved, but the signal-to-noise ratio deteriorates due to inclusion of non-contact region signals
Solution Approach 1:
The patent extracts and utilizes only the relevant subset of sensor data by identifying contact regions through contact detection and activating only light sources and detectors within these regions. This excludes signals from non-contact areas that would contribute noise, thereby improving signal-to-noise ratio while reducing the effective sensor coverage area.
3Measurement precision
If the sensor configuration is optimized for specific contact conditions, then measurement precision improves, but the adaptability to different contact scenarios deteriorates
Solution Approach 1:
The system dynamically adapts to different contact conditions by using the contact detection unit to identify contact regions in real-time and automatically reconfiguring which sensors are active. This dynamic adaptation allows the same hardware to optimize for various contact scenarios (different fingers, contact pressures, contact areas) without requiring multiple fixed configurations.
4Measurement precision
If contact area information is obtained to improve estimation accuracy, then the processing time increases due to additional detection and configuration steps
Solution Approach 1:
The system performs contact detection and sensor configuration determination as preliminary actions before actual bio-information measurement. By establishing the optimal sensor configuration in advance based on contact detection, the actual measurement phase can proceed efficiently without time-consuming adjustments during data collection.
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
Enhances the accuracy of bio-information estimation by adaptively determining sensor configurations, allowing for precise calculation of scattering coefficients and subsequent estimation of bio-parameters like triglycerides, body fat, and other biomarkers, thereby improving the reliability of non-invasive bio-sensing devices.
Implementation Method 1
Each pixel includes a light source and a detector. The processor controls the sensor part to emit light to an object and to detect light signals from the light passing through the object
Implementation Method 2
A change in blood triglyceride concentration leads to a change in scattering coefficient, such that the change in the scattering coefficient may be obtained from a change in the scattered light signals
Data Source
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AI summary
Example embodiments relate to an apparatus for non-invasively estimating bio-information is provided. An apparatus for estimating bio-information may include a sensor part including a pixel array of pixels, each pixel having a light source and a detector; and a processor configured to, based on an object being in contact with the sensor part, drive the sensor part based on a first sensor configuration; obtain contact information of the object based on an amount of light received by each pixel according to the first sensor configuration; determine a second sensor configuration based on the contact information; drive the sensor part based on the second sensor configuration; and estimate the bio-information based on light signals obtained according to the second sensor configuration.