CN119548156B - Dynamic multi-modal physiological parameter wireless acquisition system - Google Patents
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Abstract
The application relates to the technical field of wireless acquisition of physiological parameters, in particular to a dynamic multi-mode wireless acquisition system of physiological parameters. The system comprises a physiological parameter acquisition module, a physiological parameter signal modulation module, a myoelectric signal analog-to-digital conversion unit, a physiological parameter wireless communication module and a power management module, wherein the physiological parameter signal modulation module comprises a preamplifier, a myoelectric signal decomposition unit, a comparison characteristic value unit, a consistency coefficient unit, a myoelectric signal filtering unit, a myoelectric signal analog-to-digital conversion unit, a physiological parameter wireless communication module and a power management module, wherein the preamplifier is used for decomposing a myoelectric signal mode to construct an extremum distribution comparison sequence and a signal difference comparison sequence, the comparison characteristic value unit is used for obtaining comparison characteristic values of modal components, the consistency coefficient unit is used for determining consistency coefficients of myoelectric signals at different positions. The application improves the accuracy of wireless acquisition of the dynamic multi-mode physiological parameters.
Description
Technical Field
The application relates to the technical field of wireless acquisition of physiological parameters, in particular to a dynamic multi-mode wireless acquisition system of physiological parameters.
Background
The method has important significance for accurate acquisition and analysis of physiological parameters of human bodies in the fields of medical diagnosis, rehabilitation therapy, sports science and the like. For example, by monitoring the electrical activity of muscles (e.g., surface electromyographic signals), the functional status of the muscles, including the degree of recruitment, fatigue, cramping, and coordination among the muscles, can be understood, helping to diagnose neuromuscular disease, evaluate rehabilitation training effects, and optimize exercise training protocols.
In a multi-modal physiological parameter acquisition system, interference between signals is easily generated due to the simultaneous operation of multiple sensors. For example, when the electrical stimulation signal and the ultrasonic signal are simultaneously operated in the same device, electromagnetic interference or ultrasonic interference may occur, resulting in signal distortion, which affects the accuracy of measurement and the stability of therapeutic effect.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide a dynamic multi-mode physiological parameter wireless acquisition system, which adopts the following technical scheme:
The application provides a dynamic multi-mode physiological parameter wireless acquisition system, which comprises:
The physiological parameter acquisition module 10 is used for acquiring myoelectric signals of different positions of a user through each myoelectric sensor;
The physiological parameter signal modulation module 20 includes:
A preamplifier 21 for amplifying the electromyographic signal;
The electromyographic signal decomposition unit 22 is configured to perform modal decomposition of different frequencies on the electromyographic signal, respectively fit a maximum value and a minimum value in the modal components to obtain a first fitted curve and a second fitted curve, construct an extremum distribution comparison sequence of each modal component according to the distribution conditions of the first fitted curve and the second fitted curve, and construct a signal difference comparison sequence of each modal component by combining the signal difference degree between the first fitted curve and the second fitted curve;
a contrast characteristic value unit 23, configured to analyze differences of different modal components with respect to the extremum distribution contrast sequence and degrees of differences with respect to the signal difference contrast sequence, and obtain contrast characteristic values of the modal components;
The consistency coefficient unit 24 is used for analyzing the similarity degree of the contrast characteristic values of the modal components of the electromyographic signals at different positions and the distance relation of the electromyographic signal acquisition positions to determine consistency coefficients of the electromyographic signals at different positions;
the electromyographic signal filtering unit 25 is configured to cluster all the collected electromyographic signals according to the consistency coefficient, adjust a filtering window when the electromyographic signals in the cluster are filtered according to the contrast characteristic values of all modal components of the electromyographic signals in the cluster, and perform filtering processing on the electromyographic signals;
an electromyographic signal analog-to-digital conversion unit 26, configured to perform analog-to-digital conversion on the electromyographic signal after the filtering process;
the physiological parameter wireless communication module 30 is configured to send the analog-to-digital converted data to the receiving end;
the power management module 40 is configured to provide power to the wireless acquisition system.
The construction process of the extremum distribution comparison sequence of each modal component comprises the following steps:
And aiming at the first fitting curve and the second fitting curve of each modal component, respectively constructing characteristic sequences of each peak in each fitting curve according to peak information in each fitting curve, calculating the similarity between the characteristic sequences of each peak in the first fitting curve and the characteristic sequences of each peak in the second fitting curve, and forming the extremum distribution comparison sequence of the modal component by all the calculated similarities.
Wherein, the characteristic sequence of each peak consists of peak width, peak value and peak position data of each peak.
The construction process of the signal difference comparison sequence of each modal component comprises the following steps:
Respectively counting a myoelectric level mean value, an integral myoelectric value and a root mean square value of the first fitting curve and the second fitting curve aiming at the first fitting curve and the second fitting curve of each modal component;
and respectively calculating the absolute value of the difference value of the average myoelectric value corresponding to the first fitting curve and the second fitting curve, the absolute value of the difference value of the integral myoelectric value and the difference value of the root mean square value, and taking a sequence formed by the three absolute values as a signal difference comparison sequence of the modal components.
Wherein the contrast characteristic value unit 23 is configured to:
Calculating contrast characteristic values of the modal components: Wherein, the method comprises the steps of, Represent the firstContrast feature values of the individual modal components; Represent the first And (b)Euclidean distance between extremum distribution comparison sequences of the individual modal components; Represent the first And (b)The signal differences of the individual modal components compare the euclidean distance between the sequences.
Wherein the consistency coefficient unit 24 is configured to:
For each electromyographic signal, the contrast characteristic values of all modal components of the electromyographic signal are arranged in descending order according to the frequencies corresponding to the modal components to form a contrast vector of the electromyographic signal;
and calculating consistency coefficients of the electromyographic signals at different positions through the similarity degree between the contrast vectors of the electromyographic signals acquired at different positions and the distance relation between the different positions.
The formula for calculating the consistency coefficient specifically comprises the following steps: Wherein, the method comprises the steps of, Represent the firstPosition and the firstConsistency coefficients of electromyographic signals at the respective positions; And Respectively represent the firstAnd (b)A contrast vector of electromyographic signals for each location; representing cosine similarity; Represent the first And (b)Euclidean distance between the electromyographic sensors at the individual locations.
Wherein the electromyographic signal filtering unit 25 is configured to:
And taking the electromyographic signals acquired at all positions as input of a clustering algorithm, taking consistency coefficients of the electromyographic signals at different positions as similarity measurement results in a clustering process, and clustering and dividing all the electromyographic signals through the clustering algorithm.
Wherein the electromyographic signal filtering unit 25 is further configured to:
The method comprises the steps of adjusting a filter window for self-adaptive filtering of all electromyographic signals in a cluster under each frequency, wherein the size of the filter window after adjustment is as follows: In which, in the process, In order to adjust the filter window size of the adaptive filtering of all electromyographic signals in the post-cluster p at the frequency f,For the adjustment coefficient of the filter window when all electromyographic signals in the cluster p are subjected to adaptive filtering at the frequency f,Representing a preset initial filter window size.
Wherein the obtaining of the adjustment coefficient further includes:
Taking the average value of the comparison characteristic values at the same positions in the comparison vectors of all the electromyographic signals in the cluster as a comparison response value of the corresponding modal component, and taking the normalization result of the comparison response value as an adjustment coefficient of a filtering window when the electromyographic signals in the cluster carry out self-adaptive filtering under the corresponding frequency of the modal component.
The application has the following beneficial effects:
The multi-modal physiological parameter wireless acquisition system comprises a sensor module, a signal modulation module, a microcontroller module, a wireless communication module and a power management module, corresponding calibration and pretreatment are carried out on different physiological parameters, and real-time optimization adjustment processing is carried out by combining the contrast characteristics of weak and interference influence in the electromyographic signal acquisition process, so that the multi-modal physiological parameter data can be accurately and dynamically acquired through the wireless acquisition system, and the accuracy of the wireless acquired dynamic multi-modal physiological parameters is improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a wireless acquisition system for dynamic multi-modal physiological parameters according to one embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a physiological parameter signal modulation module according to an embodiment of the present application;
Fig. 3 is a schematic diagram of an implementation process of filtering an electromyographic signal according to an embodiment of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application to achieve the preset aim, the following detailed description is given below of the dynamic multi-mode physiological parameter wireless acquisition system according to the application, which is provided by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The following specifically describes a specific scheme of the dynamic multi-mode physiological parameter wireless acquisition system provided by the application with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a dynamic multi-mode physiological parameter wireless acquisition system according to an embodiment of the present application is shown, where the system includes:
The physiological parameter acquisition module 10 is used for acquiring myoelectric signals of different positions of a user through each myoelectric sensor.
The myoelectric sensor is used for collecting data, wherein the myoelectric sensor electrode is attached to the skin of a human body according to a standard electrocardio electrode placement position, is placed at a chest lead position in the embodiment, ensures good contact between the electrode and the skin in the data collecting process, and acquires the myoelectric signal.
The physiological parameter signal modulation module 20 includes a preamplifier 21, a myoelectric signal decomposition unit 22, a contrast characteristic value unit 23, a consistency coefficient unit 24, a myoelectric signal filtering unit 25, and a myoelectric signal analog-to-digital conversion unit 26.
In particular, in this embodiment, the composition diagram of the physiological parameter signal modulation module is shown in fig. 2.
The pre-amplifier 21 is arranged in order to ensure that the signal can be accurately identified and processed by a subsequent circuit in view of the weak characteristic of the electromyographic signal, the amplitude range of the pre-amplifier is 0-5000 mu V, the gain of the pre-amplifier is 100-1000 times, the pre-amplifier is ensured not to be distorted after being amplified, and meanwhile, the pre-amplifier is subjected to bias adjustment to ensure that the output signal is in the effective interval of subsequent analog-digital conversion, and the signal is realized by arranging a bias resistor or using an adjustable bias voltage source in the amplifier circuit. By accurately measuring the direct current component of the electromyographic signal and combining the input range requirement of an analog-to-digital converter (ADC), the offset voltage value is calculated, so that the electromyographic signal after pre-amplification can fall in the effective conversion interval of the ADC completely, and the problem of overlarge signal interception or quantization error is avoided. It should be noted that, the calculation of the bias voltage value and the amplifying process of the pre-amplifier to the signal are all known techniques by those skilled in the art, and will not be described in detail.
The electromyographic signal decomposition unit 22 is configured to perform modal decomposition of different frequencies on the electromyographic signal, and fit the maxima and minima in the modal components to obtain a first fitted curve and a second fitted curve, construct an extremum distribution comparison sequence of each modal component according to the distribution conditions of the first fitted curve and the second fitted curve, and construct a signal difference comparison sequence of each modal component by combining the signal difference degrees between the first fitted curve and the second fitted curve.
The filter parameters are designed in consideration of the frequency characteristics of the electromyographic signals and the frequency conditions of interference sources, and particularly, when the surface electromyographic signals are collected, the quality of the collected surface electromyographic signals is poor due to the fact that the collection environment is complex and the interference of power frequency noise and peak amplitude exists, and the traditional signal filtering method is difficult to accurately process the complex interference in the surface electromyographic signals. Therefore, according to the interference characteristics presented in the electromyographic signal acquisition process, the filter parameters are unfolded, optimized and adjusted in real time, so that the quality of the acquired electromyographic signals is improved. Specifically, in this embodiment, the steps of optimizing and adjusting the collected electromyographic signal filtering process are as follows:
in general, in the surface electromyographic signal acquisition process, the surface electromyographic signal is usually weak, and the interference characteristics of the electromyographic signal in different frequency ranges are further highlighted after the processing by using an amplifier before the signal processing, so that the electromyographic signal acquired by each electromyographic sensor is taken as input, and the modal component of each electromyographic signal under different frequencies is acquired by adopting an empirical mode decomposition algorithm and is used for analyzing the interference degree of the decomposition signals in different frequency ranges and the salient characteristics of the modal components in different frequency ranges under noise interference.
In order to further determine the peak or longer-range staged continuous influence characteristics of the electromyographic signals under the influence of interference in different frequency ranges, for each modal component of each electromyographic signal, acquiring minimum value points and maximum value points in the modal component, respectively taking all the minimum value points and the maximum value points as input, performing curve fitting by adopting a least square method, and respectively taking the obtained fitting curves as a first fitting curve and a second fitting curve of the modal component.
Considering the characteristic that the electromyographic signals show symmetrical amplitude variation in the actual acquisition process, for each modal component, a first fitting curve and a second fitting curve are respectively taken as input, the findpeak function in MATLAB is adopted to obtain the peak width, the peak value and the position data of the peak in the fitting curve, and the vector formed by the peak width, the peak value and the position of each peak in the fitting curve is taken as the characteristic sequence of each peak.
Further, the similarity between the characteristic sequence of each peak in the first fitted curve and the characteristic sequence of each peak in the second fitted curve is calculated, where the similarity may be calculated by cosine similarity or dot product, and in this embodiment, cosine similarity is used. And reflecting the prominent symmetrical difference of the electromyographic signals caused by the periodic interference of the peak or a longer time range in the signal acquisition process of the electromyographic signals under the frequency corresponding to the modal component by the interference comparison sequence.
In order to sufficiently compare the symmetric differences of the myoelectric signals in different frequency ranges, the mean value of the myoelectric level, the integral myoelectric value and the root mean square value of the first fitting curve and the second fitting curve are calculated respectively, the mean value of the myoelectric level, the integral myoelectric value and the root mean square value of the fitting curve are the known technology for the skilled person to analyze the time domain signals of the myoelectric signals, and the detailed calculation process is not repeated. Further, the difference value of the average myoelectricity value corresponding to the first fitting curve and the second fitting curve is calculated to obtain an absolute value, the difference value of the integral myoelectricity value is calculated to obtain the difference value of the absolute value and the root mean square value to obtain the absolute value, and a sequence formed by the three absolute values is used as a signal difference comparison sequence of corresponding modal components, wherein the signal difference comparison sequence reflects the integral symmetrical difference of signals affected by interference in different frequency ranges.
The contrast characteristic value unit 23 is configured to analyze differences of different modal components with respect to the extremum distribution contrast sequence and degrees of differences with respect to the signal difference contrast sequence, and obtain contrast characteristic values of the modal components.
Further, considering that interference is affected in the electromyographic signal acquisition process, there is a large difference in interference characteristics in different frequency ranges, if the symmetrical difference in the frequency ranges due to the interference is larger, the larger the interference influence of the frequencies in the range is, so that the comparison characteristic value of the interference of the electromyographic signal in each frequency range is calculated based on the characteristics of interference comparison between different frequency rangesThe specific calculation relation is as follows:
Wherein, the method comprises the steps of, Represent the firstContrast feature values of the individual modal components; Represent the first And (b)Euclidean distance between extremum distribution comparison sequences of the individual modal components; Represent the first And (b)The euclidean distance between the signal difference comparison sequences of the individual modal components, it should be noted that the practitioner may also use the manhattan distance and the framingly distance to measure the difference between the sequences, which is not particularly limited in this embodiment; Representing the number of modal components, the larger the calculated contrast characteristic value is, the description of the first The contrast difference affected by the interference in the frequency range corresponding to each modal component is large.
And the consistency coefficient unit 24 is used for analyzing the similarity degree of the contrast characteristic values of the modal components of the electromyographic signals at different positions and the distance relation of the electromyographic signal acquisition positions to determine consistency coefficients of the electromyographic signals at different positions.
In the electromyographic signal acquisition process, signal data of a plurality of parts are generally acquired, and the acquisition process is generally in the same environmental space range, so that data with similar interference influence characteristics are analyzed based on the interference influence characteristics in the common space where the data are acquired by different parts, wherein for each electromyographic signal, the contrast characteristic values of all modal components are arranged according to the frequency descending order corresponding to the modal components to form a vector, and the vector is used as a contrast vector of the electromyographic signal, and the consistency coefficient of the electromyographic signals at different positions is calculated based on the similarity between the contrast vectors and the relative positions of the electromyographic signal acquisition, and in the embodiment, the specific calculation relational expression is as follows:
Wherein, the method comprises the steps of, Represent the firstPosition and the firstConsistency coefficients of electromyographic signals at the respective positions; And Respectively represent the firstAnd (b)A contrast vector of electromyographic signals for each location; representing cosine similarity; Represent the first And (b)Euclidean distance between the electromyographic sensors at the individual locations. It can be understood that the larger the calculated consistency coefficient is, the larger the consistency of the electromyographic signals acquired at different positions under different frequency ranges is influenced by comparing the distances.
And the electromyographic signal filtering unit 25 is used for clustering all the acquired electromyographic signals according to the consistency coefficient, and adjusting a filtering window in the process of filtering the electromyographic signals in the cluster according to the contrast characteristic values of all modal components of the electromyographic signals in the cluster.
Further, all the electromyographic signals are clustered and divided according to the consistency coefficient of the electromyographic signals of the unused positions. Specifically, all the electromyographic signals acquired at the positions are used as input, the consistency coefficients of the electromyographic signals at different positions are used as similarity measurement results in the clustering process, and hierarchical clustering is adopted to acquire the division results of the electromyographic signals acquired at all the positions. It should be noted that, the specific clustering dividing process of hierarchical clustering is in the prior art, and is not described in detail in this embodiment.
Further, for each cluster in the division result, taking an average value of comparison characteristic values at the same position in comparison vectors of all electromyographic signals in the cluster as a comparison response value of a corresponding modal component, taking all the comparison response values as input, acquiring normalization results of all the comparison response values by adopting a Softmax function, taking an adjustment vector formed by all the normalization results as an adjustment coefficient of a filter window size when all the electromyographic signals in the cluster are subjected to filter processing, wherein a filter window for self-adaptive filter is adjusted in the same frequency range for all the electromyographic signals in the same cluster, and the formula of the adjusted filter window size is as follows: In which, in the process, In order to adjust the filter window size of the adaptive filtering of all electromyographic signals in the post-cluster p at the frequency f,For the adjustment coefficient of the filter window when all electromyographic signals in the cluster p are subjected to adaptive filtering at the frequency f,Representing a preset initial filter window size, which in this embodiment is set to 5 times the signal period. By comprehensively judging the electromyographic signals with similar interference influence characteristics in the cluster, the larger the interference influence is, the larger the required filter window size is, so that more accurate filter processing is performed.
Specifically, in this embodiment, a schematic implementation process of filtering the electromyographic signal is shown in fig. 3.
The electromyographic signal analog-to-digital conversion unit 26 is configured to perform analog-to-digital conversion on the electromyographic signal after the filtering process.
An analog-to-digital converter (ADC) chip of suitable resolution and sampling frequency is selected based on the requirements of data accuracy and sampling speed. Specifically, in this embodiment, for the electromyographic signal, the sampling frequency is set to 2000Hz to meet the requirement of signal analysis. And the working mode and the reference voltage parameters of the ADC are configured, so that the normal working of the ADC is ensured. And connecting the modulated analog signal to the input end of the ADC, starting the ADC conversion, and timely reading the converted data.
The physiological parameter wireless communication module 30 is configured to send the analog-to-digital converted data to the receiving end.
The wireless communication module is initialized, and the working parameters of the module, including communication frequency, baud rate and device name, are set according to the protocol specification of the selected wireless communication technology. There are many existing wireless communication technologies, such as bluetooth modules and WiFi modules.
Further, a connection with an external device is established. For the Bluetooth module, the Bluetooth module is used for connecting to a designated wireless network hot spot or configured as an access point mode, and in the embodiment, the Bluetooth module is used for connecting.
And packaging the converted data according to a format specified by a communication protocol, and transmitting the data to a wireless communication module through a communication interface. For example, the data is encapsulated into a specific data packet structure, including a data header, data content and check bit information, to ensure the accuracy and integrity of data transmission.
Specifically, the data is encapsulated according to a format specified by a Bluetooth protocol, and then a transmitting function of a Bluetooth driver is called to transmit the data to the Bluetooth module. After the Bluetooth module receives the data, the data is sent out in a wireless signal mode through an antenna. In the receiving end, such as a mobile phone and a computer, in this embodiment, the terminal device is the computer, and starts the bluetooth function, searches nearby bluetooth devices, and performs pairing connection after finding out the bluetooth devices of the acquisition system. After successful pairing, the receiving end equipment can receive the data from the acquisition system.
The power management module 40 is configured to provide power to the wireless acquisition system.
And selecting a battery with proper capacity and type according to the power consumption and the endurance requirements of the system. For example, a small lithium battery may be selected for a low power harvesting system, and a larger capacity battery may be selected for a system requiring continuous operation for a long period of time.
The battery is arranged in a battery compartment of the equipment, so that the firm installation of the battery is ensured, and the positive electrode and the negative electrode are connected correctly. For rechargeable batteries, the position and design of the charging interface also need to be considered, so that the battery can be charged conveniently.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present application are intended to be included within the scope of the present application.
Claims (8)
1. A dynamic multi-modal physiological parameter wireless acquisition system, the system comprising:
the physiological parameter acquisition module (10) is used for acquiring myoelectric signals of different positions of a user through each myoelectric sensor;
the physiological parameter signal modulation module (20) includes:
a preamplifier (21) for amplifying the electromyographic signal;
The electromyographic signal decomposition unit (22) is used for carrying out modal decomposition of different frequencies on electromyographic signals, respectively carrying out fitting on maximum values and minimum values in modal components to obtain a first fitted curve and a second fitted curve, respectively constructing characteristic sequences of peaks in each fitted curve according to peak information in each fitted curve, calculating similarity between the characteristic sequences of each peak in the first fitted curve and the characteristic sequences of each peak in the second fitted curve, forming extremum distribution contrast sequences of the modal components by all the calculated similarity, respectively counting myoelectric level average values, integral myoelectric values and root mean square values of the first fitted curve and the second fitted curve for the first fitted curve and the second fitted curve of each modal component, respectively calculating the difference value of the corresponding average myoelectric values between the first fitted curve and the second fitted curve to obtain an absolute value, the difference value of the integral myoelectric values to obtain the absolute value and the difference value of the root mean square value, and taking the absolute value of the difference value of the integral myoelectric values to form a signal difference contrast sequence of the modal components;
The contrast characteristic value unit (23) is used for analyzing the difference of different modal components about the extremum distribution contrast sequence and about the difference degree of the signal difference contrast sequence to acquire the contrast characteristic value of each modal component;
the consistency coefficient unit (24) is used for analyzing the similarity degree of the contrast characteristic values of the modal components of the electromyographic signals at different positions and the distance relation of the electromyographic signal acquisition positions and determining consistency coefficients of the electromyographic signals at different positions;
The electromyographic signal filtering unit (25) is used for clustering all the acquired electromyographic signals according to the consistency coefficient, adjusting a filtering window in the process of filtering the electromyographic signals in the cluster according to the contrast characteristic values of all modal components of the electromyographic signals in the cluster, and filtering the electromyographic signals;
the myoelectric signal analog-to-digital conversion unit (26) is used for performing analog-to-digital conversion on the myoelectric signal subjected to the filtering treatment;
The physiological parameter wireless communication module (30) is used for sending the data after analog-to-digital conversion to the receiving end;
And the power management module (40) is used for providing power for the wireless acquisition system.
2. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 1 wherein the characteristic sequence of each peak consists of peak width, peak value and peak position data for each peak.
3. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 1, characterized in that the contrast feature value unit (23) is configured to:
Calculating contrast characteristic values of the modal components: Wherein, the method comprises the steps of, Represent the firstContrast feature values of the individual modal components; Represent the first And (b)Euclidean distance between extremum distribution comparison sequences of the individual modal components; Represent the first And (b)The signal differences of the individual modal components compare the euclidean distance between the sequences.
4. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 1, wherein the consistency coefficient unit (24) is configured to:
For each electromyographic signal, the contrast characteristic values of all modal components of the electromyographic signal are arranged in descending order according to the frequencies corresponding to the modal components to form a contrast vector of the electromyographic signal;
and calculating consistency coefficients of the electromyographic signals at different positions through the similarity degree between the contrast vectors of the electromyographic signals acquired at different positions and the distance relation between the different positions.
5. The wireless acquisition system of dynamic multi-modal physiological parameters as set forth in claim 4 wherein the calculation formula of the consistency coefficient is specifically: Wherein, the method comprises the steps of, Represent the firstPosition and the firstConsistency coefficients of electromyographic signals at the respective positions; And Respectively represent the firstAnd (b)A contrast vector of electromyographic signals for each location; representing cosine similarity; Represent the first And (b)Euclidean distance between the electromyographic sensors at the individual locations.
6. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 1, characterized in that the electromyographic signal filtering unit (25) is configured to:
And taking the electromyographic signals acquired at all positions as input of a clustering algorithm, taking consistency coefficients of the electromyographic signals at different positions as similarity measurement results in a clustering process, and clustering and dividing all the electromyographic signals through the clustering algorithm.
7. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 1, wherein the electromyographic signal filtering unit (25) is further configured to:
The method comprises the steps of adjusting a filter window for self-adaptive filtering of all electromyographic signals in a cluster under each frequency, wherein the size of the filter window after adjustment is as follows: In which, in the process, In order to adjust the filter window size of the adaptive filtering of all electromyographic signals in the post-cluster p at the frequency f,For the adjustment coefficient of the filter window when all electromyographic signals in the cluster p are subjected to adaptive filtering at the frequency f,Representing a preset initial filter window size.
8. The dynamic multi-modal physiological parameter wireless acquisition system as set forth in claim 7 wherein the acquisition of the adjustment coefficients further includes:
Taking the average value of the comparison characteristic values at the same positions in the comparison vectors of all the electromyographic signals in the cluster as a comparison response value of the corresponding modal component, and taking the normalization result of the comparison response value as an adjustment coefficient of a filtering window when the electromyographic signals in the cluster carry out self-adaptive filtering under the corresponding frequency of the modal component.
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