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

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.

Description

technical field [0001] The invention relates to the field of signal processing, and is a method for removing motion and noise interference, which combines a normalized least mean difference (NLMS) adaptive filter and a Mallat algorithm, and is suitable for wearable devices based on photoplethysmography Type heart rate monitoring equipment, which can effectively eliminate motion and noise interference, and improve the accuracy of heart rate monitoring values. Background technique [0002] Heart rate refers to the number of times the human heart beats per minute. In the detection of human body parameters, heart rate is a very important physiological indicator, which provides a reference for medical diagnosis. At the same time, heart rate can also be used as an objective evaluation index of the physiological load of human exercise, and has been widely used in various aspects of fitness exercises and competitive sports training. At present, heart rate monitors still have many ...

AI 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 to achieve

The NLMS adaptive filter denoising scheme proposed by Han H et al. uses the signal collected by the three-axis accelerometer as the filter reference signal, and eliminates the noise interference according to the strong correlation between the motion acceleration and the motion noise interference. Collect signal, bulky, not easy to wear

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