Non-contact heart rate measurement method, system and device based on end-to-end network

Abstract

The invention belongs to the field of data identification, particularly relates to a non-contact heart rate measurement method, system and device based on an end-to-end network, and aims to solve theproblem of how to perform non-contact heart rate measurement in a non-contact manner and improve the robustness and generalization ability of the measurement method. The method comprises the steps ofobtaining a to-be-detected video frame sequence containing face information, obtaining a face region-of-interest image sequence through a face detection model based on the to-be-detected video frame sequence, obtaining a face key point position sequence through a face key point model, and obtaining an average heart rate value in a time period through the trained end-to-end double-branch network based on the face region-of-interest image sequence.. The robustness and generalization ability of the non-contact heart rate detection method are improved, the heart rate range capable of being accurately measured is expanded, and the heart rate measurement accuracy is improved.

Description

technical field [0001] The invention belongs to the field of data identification, and in particular relates to a non-contact heart rate measurement method, system and device based on an end-to-end network. Background technique [0002] Heart rate is an important indicator that needs to be measured on many occasions, especially in the field of health. In traditional devices, monitoring heart rate and heart activity is accomplished by measuring electrophysiological signals with an electrocardiogram (ECG) or photoplethysmography (PPG), which requires electrodes attached to the body to measure heart tissue. The signal of the electrical activity caused by PPG can only be measured by contacting the device with the human body. The contact device makes the person being tested feel uncomfortable, especially for the surge in demand for non-contact medical care during the epidemic. In order to solve this problem, recent In recent years, the technology of measuring heart rate through f...

AI Technical Summary

Problems solved by technology

The existing methods of testing heart rate through faces still have the following shortcomings: First, the existing data set is not large enough, so we can only use a shallow neural network, and the learned model is relatively easy to overfit, and generally speaking, In the case of sufficient training data, the deeper neural network has stronger discrimination and generalization capabilities; secondly, the task of measuring heart rate through the face is easily affected by head movement, light changes and different equipment; again different losses The function also has a great influence on the result

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