Method and device for estimating heart rate in various fitness states for wearable heart rate monitoring equipment

Abstract

The invention discloses a method and device for estimating heart rate in various fitness exercise states for wearable heart rate monitoring equipment. The present invention includes four parts: sparse dictionary learning, motion state recognition, motion artifact removal, and heart rate spectrum peak tracking. The pulse wave signal and noise estimation signal in a certain motion state are used as training sets, and obtained by solving the optimization problem of sparse dictionary learning. Then extract the feature information of the motion signal and identify the motion state based on the classification method to obtain the motion state corresponding to the pulse wave signal in the current time window; then judge whether the pulse wave signal contains A lot of noise, select the sparse dictionary corresponding to the current exercise state, use the sparse representation method to remove the noise interference on the pulse wave signal, and obtain the reconstructed pulse wave signal; finally, locate the peak of the heart rate spectrum in each time window. The heart rate estimation of the present invention has high precision and low complexity.

Description

technical field [0001] The invention relates to the field of biomedical signal processing, in particular to a heart rate estimation method and device for wearable heart rate monitoring equipment. Background technique [0002] Heart rate monitoring based on modern wearable devices has attracted attention due to its effectiveness in controlling training load or fitness health monitoring. Heart rate monitoring based on photoplethysmography is popular because of its portability and low cost. However, heart rate monitoring based on photoplethysmography signals during exercise is a difficult challenge because photoplethysmography signals are susceptible to motion artifacts. [0003] In fitness exercises, photoplethysmography (PPG) signals are easily affected by motion artifacts, the main source of which is some voluntary or involuntary movements of the subject, which may cause a gap between the sensor of the hand and the skin during exercise. The gap between them expands. Motio...

AI Technical Summary

Problems solved by technology

However, since the performance of most existing schemes is only evaluated using some specific motions, such as walking and running

In practice, since there are many different types of sports, such as squats and elliptical machines, heart rate monitoring must be performed in many different types of exercise states, where hand movements have different directions, leading to more complex Motion artifacts make heart rate monitoring a big challenge

Images