Real-Time Biosignal Segmentation and Fiducial Alignment
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Solution Overview
Problem
Conventional beat detection methods for wearable sensor devices struggle with accurately detecting biosignals due to user motion, signal complexity, and noise, leading to unreliable identification of feature points in signals like BCG, PPG, ECG, and GSR.
Innovation Solution
A real-time signal segmentation and fiducial points alignment framework that determines signal type, segments signals, and identifies fiducial points using techniques such as time-delay embedding and dynamic time warping, enabling robust feature alignment and detection even in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional beat detection methods are used, then the system is simple to operate, but the measurement precision and reliability of biosignal detection deteriorate due to motion, noise, and signal complexity
Solution Approach 1:
The patent segments the continuous biosignal into discrete segments based on detected fiducial points (peaks, valleys, characteristic waveforms). This segmentation allows the system to process and analyze specific portions of the signal independently, improving measurement precision by focusing on relevant features while reducing the impact of motion artifacts and noise in other portions.
Solution Approach 2:
The patent introduces an intermediary framework that includes signal quality assessment, fiducial point detection, and segment alignment algorithms. This intermediary layer processes the raw biosignal before final analysis, acting as a mediator that enhances detection accuracy by filtering and aligning segments based on their quality and temporal relationships, thereby resolving the contradiction between precision and complexity.
2Reliability
If signal segmentation and fiducial points alignment framework is implemented, then the reliability of feature point identification improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by detecting fiducial points and segmenting the signal before final analysis. By pre-identifying key features and organizing the signal into meaningful segments, the system establishes a reliable foundation for subsequent processing. This preliminary organization improves feature point identification reliability while managing complexity through structured preprocessing.
Solution Approach 2:
The patent implements feedback mechanisms where signal quality is continuously assessed and used to adjust processing parameters. The system monitors detection confidence, signal quality metrics, and alignment accuracy, then adapts its processing strategy accordingly. This feedback loop enhances reliability by ensuring only high-quality segments are used for final measurements while maintaining manageable complexity through adaptive processing.
3Adaptability or versatility
If multiple signal processing techniques (time-delay embedding, dynamic time warping) are used, then the adaptability to different signal types improves, but the ease of operation and device complexity worsen
Solution Approach 1:
The patent implements a universal processing framework that can handle multiple signal types (ECG, PPG, BCG, respiration) through a single integrated system. The framework uses common algorithms like dynamic time warping and time-delay embedding that are applicable across different biosignal types, making the system versatile and adaptable while maintaining a consistent user interface and operation流程.
Solution Approach 2:
The patent adjusts processing parameters dynamically based on the detected signal type and quality. Different biosignal types have different optimal processing parameters (sampling rates, filtering thresholds, alignment tolerances), and the system automatically adapts these parameters to match the specific signal being analyzed. This parameter adaptation enhances versatility while keeping the system easy to operate through automatic configuration.
Data Source
AI summary
Provided is an electronic device to monitor a user's biological measurements, where a sensor is configured to acquire a first signal from a user, and a diagnostic processor is configured to pre-process the first signal to generate a second signal, segment the second signal to form signal segments, determine at least one event location for each of the signal segments, match adjacent signal segments for feature alignment, and provide a third signal using results of the feature alignment.