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Signal reconstruction method of body sensor network with spatio-temporal correlation characteristics

A spatiotemporal correlation and signal reconstruction technology, applied in electrical components, code conversion, etc., can solve the problems of low reconstruction accuracy and the inability of compressed sensing reconstruction methods to fully utilize the prior knowledge of spatial and temporal correlation features.

Active Publication Date: 2018-07-03
苏州康迈德医疗科技有限公司
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Problems solved by technology

[0015] In order to solve the above technical problems, the present invention provides a signal reconstruction method of the body sensor network with the characteristics of time and space correlation, which can solve the problem that the existing compressed sensing reconstruction method cannot make full use of the gap between the signals of multiple sensor nodes in the body sensor network. Prior knowledge of spatio-temporal related features, resulting in low reconstruction accuracy

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  • Signal reconstruction method of body sensor network with spatio-temporal correlation characteristics
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  • Signal reconstruction method of body sensor network with spatio-temporal correlation characteristics

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[0068] Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0069] In order to achieve the purpose of the present invention, as Figure 1-3 As shown, in some implementations of the body sensor network signal reconstruction method with spatiotemporal correlation characteristics of the present invention, it includes the following implementation steps:

[0070] Step 1. Assume that the raw data collected by multiple sensors in the body sensor network is The random measurement matrix is The compressed data is Wherein, L is the number of sensors in the body sensor network, N is the original data length collected by each sensor, and M is the compressed data length;

[0071] Step 2. Assuming that there is synchronization (block consistency) among multiple sensors in the body sensor network, it can be represented by a JSM-2 joint sparse model, that is, X can be represented by blocks as

[0072]

[00...

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Abstract

The invention discloses a signal reconstruction method of a body sensor network with time-space correlation characteristics, which comprises the following steps: (1) Assuming that the original data collected by multiple sensors in the body sensor network is a random measurement matrix and compressed data The noise data is: Y=ΦX+V; (2) Assuming that there is synchronization among multiple sensors in the body sensor network, it is represented by a JSM‑2 joint sparse model; (3) Randomly select a sensor source to use a single measurement Vector signal reconstruction methods restore the original signal from the compressed data.

Description

technical field [0001] The invention relates to a signal reconstruction method of a body sensor network, in particular to a signal reconstruction method of a body sensor network with time-space correlation characteristics. Background technique [0002] As an important branch of the Internet of Things, the body sensor network (also known as "body sensor network", "wearable sensor network", etc.) has been widely used in recent years, such as physiological parameter monitoring, chronic disease management, health watches, Fall detection, etc. However, in scenarios where real-time continuous acquisition is required, how to reduce the transmission power consumption of wireless sensor nodes and prolong the working time of sensor nodes has always been a bottleneck problem that needs to be overcome urgently. [0003] The theory of compressed sensing provides an effective solution to this problem. Compressed sensing theory breaks through the requirements of the traditional Shannon / N...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30
Inventor 郁磊郭立泉王计平熊大曦
Owner 苏州康迈德医疗科技有限公司