Multispectral Heartbeat Cycle Data Processing for Biometric Monitoring
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Solution Overview
Problem
Raw heartbeat data from multispectral sensor devices is noisy and prone to baseline shifts, making it difficult to segment into systolic and diastolic phases, leading to inaccurate biometric monitoring results.
Innovation Solution
A heartbeat cycle data device processes raw data to generate feature vectors, filters noisy channels, and applies principal component analysis to produce an average feature vector, correcting gaps and determining accurate heartbeat cycle data for reliable biometric monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raw heartbeat data is collected using multispectral sensor devices, then comprehensive biometric information can be obtained, but the data becomes noisy and prone to baseline shifts making segmentation difficult
Solution Approach 1:
The patent extracts and removes noisy wavelength channels from the multispectral data. The processor identifies and eliminates channels with high noise levels or baseline shifts, keeping only the clean channels for further analysis. This extraction of harmful elements directly resolves the contradiction by removing noise while preserving the comprehensive biometric information from remaining channels.
Solution Approach 2:
The patent combines multiple clean wavelength channels into a single averaged waveform. By merging the signals from multiple channels after removing noisy ones, the system enhances the signal-to-noise ratio and creates a more reliable heartbeat waveform for accurate segmentation and biometric monitoring.
2Loss of information
If multiple wavelength channels are processed to maintain comprehensive data, then more biometric information is available, but processing complexity increases
Solution Approach 1:
The patent extracts only the necessary clean channels from the full multispectral dataset, removing redundant or noisy channels. This selective extraction maintains the essential biometric information while significantly reducing the amount of data that requires complex processing, thereby resolving the contradiction between information completeness and processing complexity.
Solution Approach 2:
The patent merges multiple clean channels into a single averaged waveform, which consolidates the biometric information from multiple sources into one comprehensive signal. This merging reduces processing complexity by working with a single averaged waveform rather than multiple individual channels, while preserving the complete biometric information.
3Quantity of substance
If noisy channels are included in processing, then more data is available for analysis, but the signal-to-noise ratio decreases leading to inaccurate results
Solution Approach 1:
The patent identifies and extracts noisy channels from the dataset based on quality metrics such as baseline stability and signal characteristics. By removing these problematic channels, the system maintains a sufficient quantity of data from clean channels while significantly improving the signal-to-noise ratio for accurate biometric monitoring.
Solution Approach 2:
The patent combines the signals from multiple clean channels through averaging, which increases the effective data volume while improving the signal-to-noise ratio. The averaging process enhances the true signal while suppressing random noise, resolving the contradiction between data quantity and signal quality.
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
The solution enables more accurate and reliable biometric monitoring by improving signal-to-noise ratios and correcting errors in heartbeat cycle data, resulting in precise instantaneous heart rate and blood pressure determinations.
Implementation Method 1
The sensor device includes a plurality of light sources and a photodetector array, each light source and corresponding photodetector forming a channel. The photodetector array detects optical energy to generate photometric response data.
Implementation Method 2
Photoplethysmography (PPG) is an optical technique that can be used to detect volumetric changes in blood in peripheral circulation. PPG is a non-invasive method that makes measurements at the surface of the skin.
Data Source
Figure 1A~1B
Figure 2
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AI summary
A device may obtain raw heartbeat data associated with a plurality of wavelength channels. The device may generate, based on a feature vector transformation, a plurality of feature vectors, each corresponding to a respective one of the plurality of wavelength channels. The device may identify a set of selected feature vectors, from the plurality of feature vectors, based on a plurality of squares of correlation coefficients, each associated with a respective pair of the plurality of feature vectors. The device may generate, based on a principal component analysis, an average feature vector of the set of selected feature vectors. The device may determine initial heartbeat cycle data based on the average feature vector. The device may correct heartbeat cycle gaps in the initial heartbeat cycle data in order to determine final heartbeat cycle data.