Wearable PPG Artifact Removal with 3-Axis SDLMS Filtering
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Solution Overview
Problem
Wearable health monitoring devices face challenges in accurately extracting physiological information due to motion artifacts in photoplethysmography (PPG) signals, particularly during user movement, which degrade signal quality and hinder the distinction between biological information and noise.
Innovation Solution
Employing a cascaded parallel combination (CPC) architecture with sign-data least mean squares (SDLMS) filters in x-, y-, and z-dimensions, combined with a window function and smoothing function, to successively remove motion artifacts from PPG signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional digital signal processing techniques (lowpass or bandpass filtering) are used to remove motion artifacts, then the implementation is simple, but the accuracy of derived biological information is insufficient
Solution Approach 1:
The patent segments the motion artifact removal process into three independent adaptive filters, each targeting specific dimensions (x, y, z) of motion artifacts. This segmentation allows each filter to specialize in removing artifacts from particular motion directions, improving overall accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
The patent extends the filtering approach from conventional single-dimension processing to three-dimensional processing by implementing separate adaptive filters for x-, y-, and z-dimensions. This dimensional expansion comprehensively addresses motion artifacts from all spatial directions, significantly improving measurement precision during multi-directional movements
2Measurement precision
If adaptive filters are used to improve motion artifact removal accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The complex adaptive filtering task is divided into three simpler sub-tasks, with each filter handling one spatial dimension. This segmentation reduces the computational burden of each individual filter while achieving comprehensive artifact removal through their combined effect, balancing accuracy and complexity
Solution Approach 2:
The patent combines three separate adaptive filters in parallel to achieve comprehensive motion artifact removal. By merging the outputs of dimension-specific filters, the system achieves superior accuracy that exceeds what a single filter could provide, while the parallel architecture maintains computational efficiency
3Reliability
If accelerometer-based adaptive filtering is implemented to remove motion artifacts, then the signal quality improves, but the computational requirements and processing time increase
Solution Approach 1:
The signal processing is segmented into parallel independent filter operations that can be computed simultaneously. This segmentation of the filtering task into three dimension-specific operations reduces overall processing time compared to sequential approaches, while maintaining improved signal quality through comprehensive artifact removal
Solution Approach 2:
The patent replaces complex mechanical signal acquisition systems with computational signal processing. By using software-based adaptive filtering algorithms instead of hardware modifications, the system achieves improved signal reliability while keeping processing requirements manageable through efficient algorithm implementation
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
Achieves high accuracy in estimating heart rates with less than 2 beats per minute of root-mean-squared error, comparable to transmissive-type PPG sensors, even during rest and exercise.
Implementation Method 1
PPG is an electro-optic sensing technology. Changes in blood volume and tissue composition are observed by illuminating the skin with a light emitting diode (LED) and measuring the reflected or transmitted light using a photodetector.
Implementation Method 2
An adaptive filter is a digital system with a dynamic transfer function. The transfer function coefficients are updated according to a specified optimization algorithm.
Data Source
AI summary
The disclosed system improves the physiological estimates (e.g., heart rate, pulse oxygenation, etc.) extracted from photoplethysmography (PPG) signals captured by wearable health monitors (smart watches, fitness trackers, etc.) by employing three sign-data least mean squares (SDLMS) filters in a cascaded parallel combination (CPC) that each successively remove motion artifacts in the x-, y-, and z-dimensions. In some embodiments, a window function eliminates spectral content that is unlikely in view of recent frequency estimates and/or smoothing function smooths the physiological estimates using historical physiological data. The disclosed motion artifact removal system can achieve a level of accuracy using signals from a reflective-type PPG sensor that is typically only achieved via a transmissive-type (e.g., finger-worn) PPG sensor at rest. Specifically, in initial testing, the system was able to estimate the heart rates with less than 2 beats per minute (BPM) of root-mean-squared (RMS) error during periods of both rest and exercise.