Wearable Ring Signal Filtering with DWT and ICA Noise Separation
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Solution Overview
Problem
Wearable devices face challenges in accurately collecting physiological data due to noise from motion artifacts and environmental factors, leading to inconsistent and inaccurate measurements.
Innovation Solution
Implementing algorithmic techniques such as discrete wavelet transform (DWT) and independent component analysis (ICA) to filter noise from physiological signals, utilizing accelerometers to establish noise references, and decomposing signals into frequency ranges to remove noise components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If wearable devices collect physiological data continuously, then data completeness is improved, but noise from motion artifacts increases
Solution Approach 1:
The patent segments the physiological signal into different frequency components using Discrete Wavelet Transform (DWT). The signal is decomposed into approximation coefficients (low frequency) and detail coefficients (high frequency), allowing selective processing of different frequency bands to remove motion artifact noise while preserving physiological information.
Solution Approach 2:
The patent extracts and removes noise components from the physiological signal using Independent Component Analysis (ICA). By identifying and separating independent components, the system extracts the noise portion (related to motion artifacts) and removes it from the signal, leaving the clean physiological data.
2Measurement precision
If noise filtering algorithms are applied to physiological signals, then signal accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary noise reduction using DWT and ICA algorithms before final physiological parameter calculation. By performing noise filtering in advance on the raw signal, the system prepares clean data for subsequent processing, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter representation of the signal by transforming it from the time domain to the frequency domain using DWT. This parameter transformation allows noise to be identified and removed more efficiently, reducing processing requirements for subsequent analysis.
3Reliability
If multiple signal processing algorithms are used, then noise reduction effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple signal processing techniques (DWT and ICA) into a unified noise reduction framework. By combining these algorithms in a coordinated manner, the system achieves superior noise reduction effectiveness while managing complexity through integrated processing rather than separate independent modules.
Data Source
AI summary
Methods, systems, and devices for noise filtering for a wearable ring device are described. The noise filtering procedure may employ one or more mathematical techniques or algorithms aimed at improving the quality of signals acquired by the wearable ring device. The device may measure a first signal including physiological phenomenon and a noise component. The first signal may undergo a discrete wavelet transform (DWT), where the input signal may be decomposed into various sets of coefficients, each set of coefficients representing a specific frequency range. The DWT may filter out noise by eliminating higher frequencies, as well as through a noise thresholding mechanism. The first signal may also undergo an independent component analysis (ICA), where the first signal is decomposed into various independent components. The ICA may filter out noise by identifying which ICA components correlate with the accelerometer measurements. The device may calculate a clean signal based on filtering out the noise.