Motion noise interference eliminating method suitable for wearable heart rate monitoring device

A noise interference, wearable technology, applied in the field of signal processing, can solve the problems of large volume, large amount of offline reference signal data, inconvenient to wear, etc., to reduce the amount of calculation and improve the effect of removal accuracy

Inactive Publication Date: 2014-11-26

BEIJING UNIV OF POSTS & TELECOMM

7 Cites 50 Cited by

AI-Extracted Technical Summary

Problems solved by technology

Although the Adaptive Step-size Least Mean Squares (AS-LMS) adaptive filter algorithm based on offline reference signals proposed by Ram M R et al. can achieve real-time denoising, the data volume of the selected offline reference signals is still large, which is difficult for Wearable devices require high computing power, which is difficult t...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Method used

[0025] The data acquisition module is composed of a near-infrared light-emitting tube, a light-receiving tube, a triaxial acceleration sensor and a gyroscope. The intensity and duration of the glow are controlled by a microprocessor. The three-axis acceleration sensor and gyroscope can be regarded as a six-axis accelerometer, and the directions of the six axes are shown in Figure 2. After being processed by the angle correction algorithm, the gravitational interference of the gravitational acceleration noise is eliminated, and the six-axis acceleration may record the movement more accurately and adapt to a wider ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Abstract

The invention discloses a motion noise interference eliminating method suitable for a wearable heart rate monitoring device based on a photo plethyamo graphy. The method can effectively eliminate motion noise interference and improve heart rate value monitoring precision. According to the method, an NLMS self-adapting filter and a Mallat algorithm are combined, and six-axis acceleration signals generated by a triaxial accelerometer and a gyroscope are selected as motion reference signals of the self-adapting filter to eliminate part of motion noise interference. The method takes full consideration of complexity and calculation amount of the algorithm, the characteristic that motion noise and heart rate noise are equal in frequency, and other noises and the heart rate noise are not overlapped in frequency is utilized, and through two efficient and low-complexity algorithms, different noises are eliminated step by step, and the purpose of calculating the heart rate in real time is achieved. The low complexity of the method guarantees practicability of the method on the wearable heart rate monitoring device.

Application Domain

Measuring/recording heart/pulse rateSpecial data processing applications

Technology Topic

Adaptive filterSelf adaptive +8

Image

Examples

Experimental program(1)

Example Embodiment

[0023] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0024] The structure of the wearable heart rate monitoring device based on the PPG signal in this example is as follows figure 1 shown. It is composed of data acquisition module, signal processing module, display module and communication module. The coordination and operation processing among multiple modules are controlled by the same microprocessor.

[0025] The data acquisition module consists of a near-infrared light-emitting tube, a light-receiving tube, a three-axis acceleration sensor and a gyroscope. The intensity and duration of the glow are controlled by a microprocessor. The three-axis acceleration sensor and gyroscope can be regarded as a six-axis accelerometer, and the six-axis directions are as follows: figure 2 shown. After being processed by the angle correction algorithm, the gravitational interference of the gravitational acceleration noise is eliminated, and the six-axis acceleration may record the movement more accurately and adapt to a wider range.

[0026] The information processing module includes a low-pass amplification filter, an A/D converter and a core method for removing motion noise interference. Because the signal obtained through the acquisition module is weak and mixed with various noises, a low-pass amplification filter is added before the A/D conversion to improve the signal quality and strength. The display module is an LED screen, which can display the heart rate value in real time and draw the heart rate waveform. The communication module can transmit the data to a computer through the bluetooth module for off-line processing and monitoring.

[0027] During heart rate monitoring, wear the heart rate monitoring device close to your wrist and perform routine daily exercise. based on image 3 The working principle of the motion noise interference elimination method is shown, and the specific steps are as follows:

[0028]A. The original signal is collected by the PPG sensor, including the PPG signal S(n), the motion noise interference M(n) and other noise interference N(n). The acceleration signal collected by the three-axis acceleration signal and the angle signal collected by the gyroscope are processed by The angle correction algorithm is calculated to obtain the six-axis acceleration signal X(n).

[0029] B.X(n) is used as the reference signal of the NLMS adaptive filter, and through the processing of the adaptive filter, the maximum approximate motion interference signal M'(n) is obtained by calculation. The difference between the signal before and after the motion interference is removed by the adaptive filter is e(n). The algorithm operates according to the normalized minimum mean square error principle, and the minimum mean square value of e(n) is the best denoising effect. The energy of the signal vector X(n), the step size obtained by the normalization calculation of parameters μn and γ, and the difference e(n) are dynamically changed by the formula shown below to dynamically change the NLMS adaptive filter coefficient W( n).

[0030] e(n)=S(n)+M(n)-M'(n)+N(n)

[0031] W ( n + 1 ) = W ( n ) + μ n γ + X T ( n ) X ( n ) e ( n ) X ( n )

[0032] C.Mallat algorithm multi-resolution filtering principle such as Figure 4 shown. e(n) is used as the input signal of the Mallat algorithm, and db9 is used as the wavelet base function to carry out four-layer decomposition and reconstruction. After correlation verification, the fourth-layer low-frequency signal is selected as the approximate heart rate signal.

[0033] D. Find and record the peak point of the approximate heart rate signal, use the difference-by-difference method to calculate the number of heartbeats per minute, and record it as the heart rate value.

[0034] A group of original signals are collected and transmitted from the communication module, and can also be processed off-line on the computer according to the motion noise interference removal method of the present invention. The NLMS adaptive filter can effectively eliminate the baseline drift caused by motion interference in the signal, so that the signal strength tends to be stable. The Mallat algorithm can eliminate a variety of noises (such as high-frequency power frequency noise or low-frequency breathing noise, etc.), leaving a smooth approximate heart rate signal with clear peaks, which is convenient for improving the monitoring accuracy of heart rate values.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to view more

PUM

Description & Claims & Application Information

We can also present the details of the Description, Claims and Application information to help users get a comprehensive understanding of the technical details of the patent, such as background art, summary of invention, brief description of drawings, description of embodiments, and other original content. On the other hand, users can also determine the specific scope of protection of the technology through the list of claims; as well as understand the changes in the life cycle of the technology with the presentation of the patent timeline. Login to view more.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to view more

Classification and recommendation of technical efficacy words

High removal accuracy
Small amount of calculation

Motion noise interference eliminating method suitable for wearable heart rate monitoring device

A noise interference, wearable technology, applied in the field of signal processing, can solve the problems of large volume, large amount of offline reference signal data, inconvenient to wear, etc., to reduce the amount of calculation and improve the effect of removal accuracy

AI-Extracted Technical Summary

Problems solved by technology

Method used

Abstract

Image

Examples

Example Embodiment

PUM

Description & Claims & Application Information

Similar technology patents

Full-automatic deburring device

Audio frequency classification method

Magnetorheological effect-based ship rust-removing handheld type wall-climbing robot device

Biological image denoising algorithm based on statistical rule

Simple method for detecting phase sequence of three-phase industrial-frequency alternating current and detection device

Classification and recommendation of technical efficacy words

Full-automatic deburring device

Biological image denoising algorithm based on statistical rule

Magnetorheological effect-based ship rust-removing handheld type wall-climbing robot device

License plate detection method based on deep learning

Gait recognition method based on inertial sensor

LSTM deep learning model-based hydropower unit fault diagnosis method and system

Large slanting view angle machine-carried SAR beam bunching mode imaging method based on non-uniform sampling

Mobile terminal cloud resource scheduling method based on Hadoop framework