Biometric Vibration Sensor R-Wave Extraction via Frequency Distribution
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Solution Overview
Problem
Conventional biometric information acquisition devices face challenges in accurately extracting R-wave waveforms from vibration signals due to attenuation and misinterpretation of pulse characteristics, leading to reduced accuracy in heart rate and stress level monitoring.
Innovation Solution
A biometric information acquisition device employs multiple candidate extraction units and evaluation indicators, including distribution ratios, to select the most accurate set of pulse position candidates from vibration signals, using bandpass filtering and frequency distribution analysis to enhance the accuracy of R-wave detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a vibration sensor is used to detect heartbeats, then the subject can be monitored without electrodes or constraints, but the pulse waveform characteristics are attenuated and difficult to extract accurately
Solution Approach 1:
The patent segments the complex vibration signal into multiple frequency components using bandpass filtering at different frequency ranges. By dividing the signal processing into multiple frequency bands and extracting pulse waveforms from each segment, the system overcomes the attenuation problem and improves extraction accuracy while maintaining non-invasive monitoring.
Solution Approach 2:
The patent introduces an intermediary processing stage that includes bandpass filtering, signal enhancement, and waveform extraction algorithms. This intermediary processing transforms the attenuated vibration signal into clear pulse waveforms, bridging the gap between the convenient vibration-based monitoring and accurate pulse detection.
2Reliability
If bandpass filtering is applied to extract resonant frequency components, then breathing and movement can be separated from heartbeats, but pulse characteristics may still attenuate or disappear
Solution Approach 1:
The patent employs dynamic signal processing that adapts to varying signal conditions. The system dynamically adjusts filtering parameters and selectively processes different frequency components based on the detected signal characteristics, ensuring pulse waveforms remain detectable even when signal conditions change.
Solution Approach 2:
The patent changes multiple processing parameters including filter frequency ranges, signal amplification levels, and waveform extraction thresholds. By optimizing these parameters dynamically, the system maintains reliable separation of breathing and movement from heartbeats while preserving pulse waveform detectability.
3Measurement precision
If multiple types of candidate extraction are performed with evaluation indicators, then the most accurate pulse position candidates can be selected, but the processing complexity and time increase
Solution Approach 1:
The patent performs preliminary filtering and candidate identification using bandpass filters to pre-process the vibration signal and identify potential pulse positions before detailed evaluation. This preliminary action reduces the number of candidates requiring complex evaluation, improving R-wave detection accuracy while controlling processing complexity.
Solution Approach 2:
The patent implements feedback mechanisms where evaluation indicators assess the quality of extracted pulse position candidates, and this feedback is used to refine subsequent extraction processes. The system uses detected pulse characteristics to adjust processing parameters, achieving high detection accuracy through iterative refinement rather than overly complex single-stage processing.
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 device achieves improved accuracy in extracting R-wave waveforms, reducing misinterpretation and enhancing the reliability of heart rate and stress level monitoring without the need for electrocardiographic electrodes.
Implementation Method 1
a vibration sensor for detecting a vibration signal including heartbeats of a person
Implementation Method 2
extract a resonant frequency component (4 to 10 Hz) of the body trunk that is generated from the pulse
Data Source
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AI summary
A plurality of pulse position candidate extraction units (38a to 38t) characterize waves included in a vibration signal from a vibration sensor (VS) using predetermined specific forms and extract respective sets of pulse position candidates. A frequency distribution calculation unit (39) calculates a frequency distribution of the pulse interval and a frequency distribution of variation in pulse intervals on each of the extracted sets of pulse position candidates. A distribution ratio calculation unit (40) calculates the distribution ratio of a modal class to the total frequency in each of the calculated frequency distributions. A selection unit (41) selects a set of pulse position candidates from among the extracted sets of pulse position candidates using the calculated relative frequencies as indicators for evaluating accuracy of the sets of pulse position candidates.