CN111669409A

CN111669409A - Sign data monitoring system

Info

Publication number: CN111669409A
Application number: CN202010673776.5A
Authority: CN
Inventors: 黄芳; 张�浩; 刘海洋; 赵坤; 龙飞; 陈立平; 杨亚光
Original assignee: Institute of Microelectronics of CAS
Current assignee: Institute of Microelectronics of CAS
Priority date: 2020-07-14
Filing date: 2020-07-14
Publication date: 2020-09-15
Anticipated expiration: 2040-07-14
Also published as: CN111669409B

Abstract

The application discloses sign data monitoring system, including wearable sensing equipment and platform, wherein wearable sensing equipment carries out analog signal processing and analog-to-digital conversion to the sign data of gathering, and further adopt the compression perception mode to carry out compression sampling to sign data, obtain compression sampling data and compare in the data volume greatly reduced of the sign data of direct acquisition, wearable sensing equipment only need with a small amount of compression sampling data send for the platform can, the power consumption of wearable equipment has been reduced, thereby operating time has been promoted, can monitor the sign of target object for a long time stably. Furthermore, the platform can carry out signal recovery on the compression sampling data to obtain the recovered physical sign data, and normal use of the physical sign data is guaranteed.

Description

Sign data monitoring system

Technical Field

The application relates to the technical field of sign data monitoring, in particular to a sign data monitoring system.

Background

In recent years, the steady development of mobile computing, integrated circuit technology, wireless sensor networks and medical devices has opened the way for miniature, low-cost, low-power consumption and multifunctional intelligent monitoring devices, which are suitable for many portable medical device applications. Wearable body area networks based on wireless and embedded monitoring devices can continuously monitor vital sign data, such as electrocardio, blood pressure, pulse, respiration, etc., and provide feedback to help maintain optimal health. These networks allow continuous, long-term, non-invasive, ubiquitous, dynamic monitoring of vital signs, revolutionizing healthcare.

Taking the monitoring of electrocardiographic data as an example, an electrocardiogram is a biological signal representing the electrical activity of the heart. It is widely used in the medical field due to its non-invasive nature and its ability to detect heart disease. Electrocardiograms are usually recorded in a hospital or clinical center where patients need to stay for hours or days. A portable or mobile electrocardiographic monitoring device enables a patient to monitor his electrocardiogram, record electrocardiographic data and transmit it to a hospital. The transmitted data will be processed at the health centre in case of any anomalies.

The existing wearable Internet of things equipment needs to continuously transmit a large amount of monitoring data back to a background, so that the power consumption is too high, the energy consumption of a battery is too fast, and the equipment cannot run for a long time.

Disclosure of Invention

In view of the above problems, the present application is proposed to provide a vital sign data monitoring system for solving the problems of high power consumption and short operation time of the existing device. The specific scheme is as follows:

a vital signs data monitoring system comprising: wearable sensing devices and platforms;

the wearable sensing equipment comprises a sign data collector, an analog front-end module, a compression sampling module and a first communication module, wherein the sign data collector is used for collecting sign data of a subject; the simulation front-end module is used for carrying out simulation signal processing on the acquired physical sign data to obtain processed physical sign data; the compression sampling module is used for performing analog-to-digital conversion on the processed physical sign data and performing compression sampling on the converted physical sign data in a compression sensing mode to obtain compression sampling data; the first communication module is used for sending the compressed sampling data to the platform;

the platform comprises a second communication module and a compressed sensing processing module, wherein the second communication module receives the compressed sampling data; and the compressed sensing processing module is used for carrying out signal recovery based on the compressed sampling data to obtain recovered sign data.

Preferably, the analog front end module comprises:

and the alternating-current coupling chopping modulation instrument amplifier and the chopping spike filter are used for filtering interference noise in the sign data.

Preferably, the analog front end module further comprises:

and the programmable gain amplifier is used for amplifying the analog signals of the sign data after the noise is filtered.

Preferably, the analog front end module further comprises:

and the buffer is used for performing stable processing on the sign data amplified by the analog signal.

Preferably, the compression sampling module comprises:

the analog-to-digital converter is used for converting the physical sign data in the form of analog signals into digital signals;

the discrete wavelet transform unit is used for carrying out sparsification on the sign data in the form of digital signals to obtain the sparsified sign data;

and the compression unit is used for compressing the thinned physical sign data to obtain compressed sampling data.

Preferably, the analog-to-digital converter is a successive approximation register analog-to-digital converter, and the sampling rate of the analog-to-digital converter is adjustable;

the discrete wavelet transform unit comprises a multi-level wavelet transform unit and a digital clock manager, and the digital clock manager generates a corresponding clock signal according to the sampling rate and the data processing power of the successive approximation register type analog-to-digital converter; the multi-level wavelet transform unit adopts multi-level decomposition wavelet transform under different clock signals to carry out sparsification on the sign data in the form of digital signals, so as to obtain the sparsified sign data.

Preferably, the compression unit includes a binary sparse matrix compression unit, and is configured to use a binary sparse matrix as a measurement matrix for compressed sensing processing, and perform compression processing on the thinned physical sign data to obtain compressed sampling data.

Preferably, the binary sparse matrix is a low density parity check matrix.

Preferably, the compressed sensing processing module includes a signal recovery module, and is configured to perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm to obtain recovered sign data.

Preferably, the wearable sensing apparatus further comprises:

the channel pre-coding module is used for carrying out channel pre-coding on the compressed sampling data based on a dynamic threshold which is returned by the platform and can embody the signal space characteristic, so that the space characteristic of the pre-coded compressed sampling data is matched with the channel condition of the first communication module;

the first communication module sends the pre-coded compressed sampling data and the output coefficient of channel pre-coding to the platform;

the compressed sensing processing module further comprises: and the dynamic threshold generating module is used for analyzing the output coefficient to generate a dynamic threshold, and the second communication module sends the generated dynamic threshold to the wearable sensing equipment.

Borrow by above-mentioned technical scheme, the sign data monitoring system of this application, including wearable sensing equipment and platform, wherein wearable sensing equipment carries out analog signal processing and analog-to-digital conversion to the sign data of gathering, and further adopt the compressed sensing mode to carry out compression sampling to sign data, obtain the data bulk greatly reduced that compression sampling data compare in the sign data of direct collection, wearable sensing equipment only need with a small amount of compression sampling data send for the platform can, the power consumption of wearable equipment has been reduced, thereby the operating time has been promoted, can monitor the sign of target object for a long time stably. Furthermore, the platform can carry out signal recovery on the compression sampling data to obtain the recovered physical sign data, and normal use of the physical sign data is guaranteed.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a schematic structural diagram of a sign data monitoring system according to an embodiment of the present application;

fig. 2 is a schematic diagram of an analog front end module according to an embodiment of the present disclosure;

FIG. 3 is a block diagram of another exemplary analog front end module according to an embodiment of the present disclosure;

FIG. 4 is a block diagram of another analog front end module according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a compressive sampling module according to an embodiment of the present disclosure;

FIG. 6 is a schematic structural diagram of a compressed sensing processing module according to an embodiment of the present disclosure;

fig. 7 is a flowchart of a signal recovery method according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of another vital sign data monitoring system according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The application provides a sign data monitoring system, solves current monitored control system high-power consumption, and operating duration short scheduling problem realizes carrying out the purpose of monitoring record to wearing person sign data for a long time, like the electrocardiosignal etc..

With reference to fig. 1, a sign data monitoring system disclosed in the embodiment of the present application is described.

As shown in fig. 1, the vital signs data monitoring system may include:

a wearable sensing device 1 and a platform 2.

The wearable sensing device 1 may include the following constituent modules:

sign data collector 11, analog front end module 12, compression sampling module 13 and first communication module 14.

The sign data collector 11 is used for collecting sign data of a subject to be monitored. According to different types of the physical sign data to be acquired, the physical sign data acquisition unit 11 can adopt different sensors, for example, the physical sign data acquisition unit can be an acquisition unit for acquiring various physical sign data such as electrocardiosignals, pulse signals, blood pressure signals and the like.

The vital sign data collected by the vital sign data collector 11 is in the form of an analog signal and thus can be transmitted to the analog front-end module 12.

The analog front-end module 12 is configured to perform analog signal processing, such as denoising, on the acquired physical sign data to obtain processed physical sign data. The processed vital sign data can be transmitted to the compression and sampling module 13 for subsequent processing.

The compression and sampling module 13 is configured to perform analog-to-digital conversion on the received physical sign data in the form of an analog signal, and convert the physical sign data into a digital signal for subsequent processing. Further, in order to reduce the data amount of the physical sign data, the compression sampling module 13 may further perform compression sampling on the physical sign data in a compression sensing manner to obtain compression sampling data.

The compressed sensing processing method can be understood as: when the signal is sparse or compressible in a certain transform domain, the transform coefficients can be projected linearly into a low-dimensional observation vector by using a measurement matrix which is incoherent with the transform matrix, and the projection keeps the information required for reconstructing the signal, so that the original high-dimensional signal can be reconstructed accurately or with high probability from the low-dimensional observation vector by further solving the sparse optimization problem.

The basic principle of the compressed sensing processing technique is mathematically explained as follows: modeling compressed sensing in matrix form as y_M×1＝Φ_M×Nx_N×1＝Φ_M×NΨ_N×Ps_P×1(ii) a Wherein x_N×1Is an input vector having N dimensions, phi_M×NIs a measurement matrix (M < N), y_M×1Is the observed measurement vector, Ψ_N×PIs a sparse basis of the input vector, and s_P×1Is the corresponding sparse coefficient vector, Φ_M×NTo Ψ_N×PA constrained isometry condition (RIP) is satisfied. s_P×1In which at most K (K < N) non-zero terms are present, then s is named_P×1Is a signal with a sparsity K, x_N×1At Ψ_N×PThe domains are sparse. The reconstruction basis in compressed sensing is to use solution l₁Norm minimization problem reconstruction of signals

The compressed sampling module 13 performs compressed sampling on the physical sign data by adopting compressed sensing processing, and the data amount of the obtained compressed sampling data is greatly reduced compared with the original physical sign data.

On this basis, the compressed sample data is sent to the platform 2 via the first communication module 14.

Since the data volume of the compressed sampling data is greatly reduced compared with the original sign data, the data volume that the first communication module 14 needs to send is smaller, the power consumption is lower, and the running time of the wearable sensing device is longer under the condition that the same electric quantity supports the wearable sensing device.

Further, the platform 2 may be a fixed or movable terminal device, such as a server, a mobile phone, a notebook, and other terminal devices in various forms, which may be specifically selected according to actual needs.

The platform 2 may include the following constituent modules:

a second communication module 21 and a compressed sensing processing module 22.

The second communication module 21 establishes communication connection with the first communication module 14, so as to realize a data communication function between the wearable sensing device 1 and the platform 2.

The first communication module 14 and the second communication module 21 may be various forms of wireless communication modes such as a bluetooth communication module and a radio frequency communication module.

The second communication module 21 is configured to receive the compressed sampled data sent by the first communication module 14, and send the compressed sampled data to the compressed sensing processing module 22.

The compressed sensing processing module 22 performs inverse signal recovery based on the compressed sampled data, that is, reconstructs the sign data before compressed sampling.

The utility model provides a sign data monitoring system, including wearable sensing equipment and platform, wherein wearable sensing equipment carries out analog signal processing and analog-to-digital conversion to the sign data of gathering, and further adopt the compressed sensing mode to carry out compression sampling to sign data, obtain the data bulk greatly reduced that compression sampling data compare in the sign data of direct collection, wearable sensing equipment only need with a small amount of compression sampling data send for the platform can, the consumption of wearable equipment has been reduced, thereby the operating time has been promoted, can monitor the sign of target object for a long time stably. Furthermore, the platform can carry out signal recovery on the compression sampling data to obtain the recovered physical sign data, and normal use of the physical sign data is guaranteed.

Further, the structure of the analog front end module 12 described above in the present application will be described.

As shown in fig. 2, the analog front end module 12 may include:

and the alternating-current coupling chopping modulation instrument amplifier 121 and the chopping spike filter 122 are used for filtering interference noise in the sign data.

For the collected physical sign data, which contains various interference noises, high input impedance is needed to suppress the DC differential electrode detuning and realize high common mode rejection ratio. In the embodiment, a chopping technology is introduced, the impedance is greater than 1G omega, and the common-mode rejection ratio is greater than 115dB at the main power frequency of 50Hz, so that the noise of interference signals is suppressed.

As further shown in fig. 3, the analog front end module 12 may further include:

and the programmable gain amplifier 123 is configured to perform analog signal amplification on the sign data after the noise is filtered.

Still further, as shown in fig. 4, the analog front-end module 12 may further include:

and the buffer 124 is used for performing smoothing processing on the sign data after the analog signal amplification.

In another embodiment of the present application, a structure of the compressive sampling module 13 is described.

As shown in fig. 5, the compressive sampling module 13 may include:

the analog-to-digital converter 131 is configured to convert the physical sign data in the form of an analog signal into a digital signal.

The analog-to-digital converter 131 may be a successive approximation register analog-to-digital converter, and the sampling rate thereof is adjustable.

And the discrete wavelet transform unit 132 is configured to perform sparsification on the sign data in the form of the digital signal to obtain the sparsified sign data.

Alternatively, the discrete wavelet transform unit 132 may include a multi-level wavelet transform unit and a digital clock manager, wherein the digital clock manager generates a corresponding clock signal according to the sampling rate and the data processing power of the successive approximation register type analog-to-digital converter 131; the multi-level wavelet transform unit adopts multi-level decomposition wavelet transform under different clock signals to carry out sparsification on the physical sign data in the form of digital signals, so as to obtain the sparsified physical sign data.

The compression unit 133 is configured to perform compression processing on the thinned physical sign data to obtain compressed sample data.

Specifically, the compressed signal with the original length N is compressed into a length M by the compression unit 133, where M is much smaller than N.

Optionally, the compression unit 133 may include a binary sparse matrix compression unit, and is configured to use a binary sparse matrix as a measurement matrix for compressed sensing processing, and compress the thinned physical sign data to obtain compressed sampling data.

The binary sparse matrix is a measurement matrix of the compressed sensing process, which may be defined by a low density parity check matrix H of size mxn.

Sparse binary sequences are those that contain an initial random distribution of a small number of 1's and a large number of 0's. These sequences and their shifted sequences at specified positions constitute a low density parity check matrix such that each sequence corresponds to a row in the matrix. To take a simple example: with two sparse sequences S₁1100001100 and S2 101111000100. If each sequence is cyclically shifted left by four positions, the low density parity check matrix based on the two sparse sequences is the following matrix:

the first and fourth rows of H correspond to S₁And S₂. The other row of H is S₁And S₂We can thus obtain a simple matrix with a block size of 6 × 12 based on both sequencesIf the parity check matrix is large, the robust rows with 4 cycles and 6 cycles can be deleted according to a certain rule to obtain the required low-density parity check matrix.

In another embodiment of the present application, a structure of the compressed sensing processing module 22 in the platform 2 is described.

As shown in fig. 6, the compressed sensing processing module 22 may include a signal recovery module 221, configured to perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm, so as to obtain recovered sign data.

The signal recovery module 221 may specifically perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm for boundary optimization to obtain recovered sign data.

Next, a process of the signal recovery module 221 performing signal recovery on the compressed sampled data based on the block sparse bayesian learning algorithm to obtain recovered sign data is introduced.

The compressed sensing basic model containing the noise vector can be described as follows:

y＝Φx+ (1)

wherein y is compression sampling data, and x is recovered sign data.

If the sparse signal x has block sparsity, it can be considered as a concatenation of blocks:

equations (3) and (4) constitute the block sparse CS model. In this model the vector x can be divided into g blocks, let each x_i(i ═ 1,2, …, g) satisfies the parameterized multivariate gaussian distribution:

p(x_i；γ_i,B_i)＝Ν(0,γ_iB_i)(i＝1,2,…,g) (3)

in the formula: gamma ray_iIs a non-negative parameter, determines block x_iSparsity of (a) when γ_iX corresponding to 0_i＝0；

Is to x_iThe positive definite matrix of the covariance structure model, representing the correlation between elements within the block structure. B is_iThe definition is as follows: a set of d-dimensional random variables X ═ X₁,x₂,…,x_d)^TThe covariance of the two random variables is cov [ x ]_i,x_j]＝E[(x_i-E[x_i])(x_j-E[x_j])]The covariance matrix consisting of d × d covariances is

When the block sizes are the same, the effective measure to avoid overfitting is parameter averaging, i.e. starting from block 2

The regulation of learning BETA is as follows:

let formula (3) be p (x)_i；{γ_i,B_i}_i)～Ν(0,Σ₀) Wherein ∑₀＝diag{γ₁B₁,…,γ_gB_g}. It is also assumed that the noise follows a p (; λ) -N (0, λ i) distribution, where λ is a positive scalar. Then

Wherein mu_x＝Σ₀Φ^T(λΙ+ΦΣ₀Φ^T)^-1y，

When the parameter a is given by the parameter a,

estimated, Maximum A Posteriori (MAP) estimates of x

Can be obtained from the posterior mean, i.e.

Estimating the parameters by class ii maximum likelihood estimation is equivalent to minimizing the following cost function L (Θ):

in the formula: theta denotes all parameters, e.g.

The original cost function L (Θ) in equation (6) consists of two parts, the 1 st part log | λ i + Φ Σ₀Φ^TL is at

Upper concave, part 2 y^T(λΙ+ΦΣ₀Φ^T)^-1y is convex at γ ≧ 0. Part 1 is chosen to find an upper bound and then minimize the upper bound of the cost function L (Θ). Let gamma^*For a given point in space, then:

wherein

Substituting formula (7) into (6) can yield:

using the alternative function:

most preferably x is μ_xThen there is

Obtaining a new function (gamma, x) defined as

The upper limit of (2):

note that (γ, x) is convex at both γ, x, and it can be easily seen

Is the solution of (γ, x), so the cost function L (Θ) is eventually replaced with (γ, x). Taking (γ, x) in relation to γ_iThe derivative of (c) can be found:

for convenience of understanding, the signal recovery module performs signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm, and a process of obtaining recovered sign data is introduced in a step flow form as follows:

as shown in connection with fig. 7, the process may include:

step S100, inputting: y, phi and eta.

Where y is the compressed sample data, Φ is the set measurement matrix, η is the exit condition, and η may be made equal to 1e^-8。

Step S110, initialization: γ is 1; λ 1e^-2||y||₂。

Step S120, calculating mu_x、Σ_x。

Wherein, mu_x、Σ_xThe calculation method can refer to the foregoing description, and is not described herein again.

Step S130, calculating

Step S140, calculating

Step S150, calculating

Step S160, judgment

If yes, the process returns to step S120, otherwise, step S170 is performed.

Step S170, outputting x ═ μ,

Wherein, the sparse x ═ mu is the reduced physical sign data,

is a parameter estimation.

In yet another embodiment of the present application, another alternative structure of the wearable sensing device 1 is presented.

As shown in fig. 8, the wearable sensing apparatus 1 may further include: and the channel pre-coding module 15 is configured to perform channel pre-coding on the compressed sampled data based on a dynamic threshold which is returned by the platform 2 and can represent the spatial characteristic of the signal, so that the spatial characteristic of the pre-coded compressed sampled data matches the channel condition of the first communication module 14.

Specifically, channel pre-coding is to pre-process the acquired signals to compensate some problems of the transmission channel, and reduce the possibility of error codes occurring in the platform receiving signals. The channel precoding module 15 may optimize the spatial characteristics of the signals to be transmitted according to the channel conditions, so that the spatial distribution characteristics of the signals are matched with the channel conditions, and the degree of dependence on the receiver algorithm may be effectively reduced.

Based on this, the first communication module 14 transmits the precoded compressed sample data and the channel-precoded output coefficients to the platform 2.

The compressed sensing processing module 22 in the platform 2 may further include: a dynamic threshold generating module 222, configured to analyze the output coefficient to generate a dynamic threshold, and send the generated dynamic threshold to the wearable sensing apparatus 1 by the second communication module 21.

The dynamic threshold generation module 222 may calculate performance parameters of the recovered sign data, such as Mean Square Error (MSE), percentage root mean square deviation (PRD (%)), signal-to-noise ratio (SNR), and the like, according to the output coefficient, and then generate a dynamic threshold according to the performance parameters, so that the performance of the sign data recovered based on the compressed sampling data meets the requirement.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, the embodiments may be combined as needed, and the same and similar parts may be referred to each other.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vital sign data monitoring system, comprising: wearable sensing devices and platforms;

2. The system of claim 1, wherein the analog front end module comprises:

3. The system of claim 2, wherein the analog front end module further comprises:

4. The system of claim 3, wherein the analog front end module further comprises:

5. The system of claim 1, wherein the compressive sampling module comprises:

6. The system of claim 5, wherein the analog-to-digital converter is a successive approximation register analog-to-digital converter, and the sampling rate of the analog-to-digital converter is adjustable;

7. The system according to claim 5, wherein the compression unit includes a binary sparse matrix compression unit, and is configured to use a binary sparse matrix as a measurement matrix for compressed sensing processing, and perform compression processing on the thinned sign data to obtain compressed sampling data.

8. The system of claim 7, wherein the binary sparse matrix is a low density parity check matrix.

9. The system according to claim 1, wherein the compressed sensing processing module comprises a signal recovery module configured to perform signal recovery on the compressed sampled data based on a block sparse bayesian learning algorithm to obtain recovered sign data.

10. The system of claim 9, wherein the wearable sensing device further comprises: