Wearable electrocardiogram monitoring network switching method based on reinforcement learning
A technology of reinforcement learning and network switching, which is applied in the fields of medical science, electrical components, diagnostic recording/measurement, etc. It can solve the problem of large amount of calculation, inapplicable real-time transmission system of physiological data, and real-time transmission of wearable devices, etc. problem, to achieve the effect of improving adaptability, improving accuracy, and reducing the amount of model calculation
Pending Publication Date: 2022-06-28
NINGBO INST OF NORTHWESTERN POLYTECHNICAL UNIV
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However, this method needs to collect information such as the location, time period, business type, and network reliability of each mobile terminal, and requires a third-party server, which requires a large amount of calculation and is not suitable for real-time physiological data transmission systems.
At the same time, it can determine the type of network used according to the usage status of network-related functions in the terminal and switch to a matching high-power or low-power network. However, this method does not take into account the real-time transmission of wearable device monitoring data, and cannot Easily judge the network type of high and low power consumption
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[0031] The specific embodiments of the present invention are described below to facilitate those skilled in the art to understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Such changes are obvious within the spirit and scope of the present invention as defined and determined by the appended claims, and all inventions and creations utilizing the inventive concept are within the scope of protection.
[0032] A network switching method for wearable ECG monitoring based on reinforcement learning, such as figure 1 shown, including the following steps:
[0033] S1. Receive the data returned by the environmental network monitoring device and the ECG data sensing module;
[0034] In this embodiment, the model receives the data returned by the network environment state monitor as input, and the environment state information such as ...
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The invention discloses a wearable electrocardiogram monitoring network switching method based on reinforcement learning. The network switching method comprises the steps of environment state input, data preprocessing, output action decision by using Qlearning, environment feedback reward receiving, Qtable parameter updating and network switching execution. Various factors in the wearable electrocardiogram monitoring environment can be comprehensively considered, the accuracy of the model is improved, meanwhile, it is guaranteed that the model calculation amount is reduced as much as possible when a correct decision is made, the instant switching task of the wearable network is efficiently completed, and the method can serve as a model basis for intelligent switching of the wearable electrocardiogram monitoring data transmission network.
Description
technical field [0001] The invention relates to the field of communication control, in particular to a wearable electrocardiogram monitoring network switching method based on reinforcement learning. Background technique [0002] Wearable ECG monitoring could improve the efficiency of healthcare. However, the current wearable devices relying only on a single wireless transmission technology cannot guarantee that patients can be connected to a remote ECG health center at any time and any place. To this end, a vertical switching scheme sensitive to the patient's health status is proposed for remote wearable ECG monitoring applications under heterogeneous wireless networks. Vertical handover in heterogeneous networks refers to the process of migrating from one network to another without interrupting service links. Therefore, in the existing communication technology, it is necessary to select the most suitable technology according to factors such as the power level, data rate, ...
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Login to View More IPC IPC(8): H04W36/14H04W36/30A61B5/256A61B5/28A61B5/332G06K9/62
CPCH04W36/14H04W36/30A61B5/256A61B5/28A61B5/332G06F18/23G06F18/24Y02D30/70
Inventor 张羽赵文娟杨慧亢羽童
Owner NINGBO INST OF NORTHWESTERN POLYTECHNICAL UNIV