Cloud computing cardiovascular health monitoring system and method based on network camera

Abstract

The invention discloses a cloud computing cardiovascular health monitoring system and method based on a network monitoring camera. The method is implemented through the following steps that 1) a front-end monitoring camera collects a face video; 2) a cloud server side remotely acquires an uploaded video data stream; 3) a ROI of the face of a user in the video is selected to perform pulse wave extraction; and (4) pulse wave noise reduction processing is performed, and three cardiovascular parameters including the heart rate, the oxyhemoglobin saturation degree and the blood viscosity are estimated by utilizing a corresponding algorithm. According to the method and system provided by the invention, a remote video stream image is transmitted by utilizing an existing network camera, the cloud server side provides the algorithm support required by an IPPG technology, and the problems that existing non-contact heart rate detection only supports local operation, continuous real-time monitoring is inconvenient are solved, so that the long-time continuous detection of the non-contact heart rate detection in a real scene can be realized.

Description

technical field [0001] The invention belongs to the field of medical video image processing, and in particular relates to an architecture of a remote cardiovascular health monitoring system using a network camera combined with cloud computing and an implementation method thereof. Background technique [0002] Cardiovascular status is an important physiological indicator that reflects human health. Its main parameters include: heart rate, blood oxygen saturation, and blood viscosity. It has been proven to be an effective diagnostic method in the fields of cardiovascular and cerebrovascular diseases, hypertension, psychological and mental diseases. According to the report released by the National Cardiovascular Center, the number of cardiovascular patients in my country is nearly 290 million, and cardiovascular disease has become the primary threat to the health of Chinese residents. Real-time monitoring of important parameters of cardiovascular health is particularly importa...

AI Technical Summary

Problems solved by technology

The existing problems are: the cost is high, and ordinary families cannot afford the high price of the electrocardiogram machine; The patient cannot complete the detection process alone, and the poor comfort is not conducive to long-term real-time detection

However, there are still some problems in the practical application of this technology. First, the process of collecting facial video first, and then analyzing and processing the video after the collection is completed, cannot satisfy real-time performance; second, only local detection can be performed. It cannot meet the requirements of remote monitoring for the elderly living alone; the third is: it does not meet the requirements of continuous long-term detection

There is no method or technology to solve or improve this problem

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