Pulse Wave Filtering With Multidimensional Motion Noise Estimation
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Solution Overview
Problem
Existing methods for estimating heart rate from pulse wave signals acquired by devices in motion suffer from relatively low estimation accuracy due to motion noise.
Innovation Solution
A signal processing method involving independent filtering on motion signals in multiple dimensions, estimating noise values for each dimension, and using these values as parallel input parameters to filter the pulse wave signal, thereby reducing noise and improving heart rate estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heart rate is estimated based on pulse wave signal acquired by device in motion, then heart rate estimation can be performed, but estimation accuracy is relatively low due to motion noise
Solution Approach 1:
The motion signal processing is segmented into multiple independent dimensional directions (e.g., X, Y, Z axes). Each dimension undergoes separate filtering processing to estimate noise components, which are then combined to remove motion artifacts from the pulse wave signal. This segmentation allows targeted noise removal while preserving physiological signal integrity.
Solution Approach 2:
Motion signals are introduced as intermediary reference inputs to the adaptive filtering process. These motion signals serve as mediators that enable the system to identify and estimate motion-induced noise components in the pulse wave signal, facilitating their removal while preserving the underlying physiological information.
2Reliability
If filtering processing is performed on pulse wave signal to remove motion noise, then signal quality improves, but processing complexity increases
Solution Approach 1:
The filtering process is divided into independent dimensional segments where each dimension's motion signal is processed separately to estimate noise components. This modular approach breaks down the complex filtering task into manageable segments that can be independently computed and then combined, reducing overall processing complexity while maintaining signal quality.
Solution Approach 2:
The system dynamically adjusts filtering parameters based on the characteristics of motion signals detected in different dimensional directions. By adapting filter coefficients and processing parameters to match the actual motion conditions, the system achieves effective noise removal without requiring overly complex fixed-structure filters.
Data Source
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AI summary
This application discloses a signal processing method and apparatus, and an electronic device and belongs to the field of signal processing technologies. The method includes: obtaining a first pulse wave signal and motion signals in multi-dimensional directions, where the first pulse wave signal includes motion noise; performing independent filtering processing on the motion signal in each dimensional direction, and determining a noise estimated value corresponding to each dimensional direction; and using the noise estimated values in the multi-dimensional directions as parallel input parameters, using the first pulse wave signal as an input parameter, and performing filtering processing on the first pulse wave signal, to obtain a second pulse wave signal.