Multi-frequency bio-electricity signal control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of signal processing, and provides a multi-frequency bio-electricity signal control method and device, equipment and a storage medium. The multi-frequency bio-electricity signal control method comprises the following steps: acquiring a target tissue parameter, and mapping the target tissue parameter to a corresponding frequency based on a pre-constructed mapping rule between the tissue parameter and the frequency; based on the spatial distribution of the target organization, space-oriented strategy configuration is carried out; and dividing a control period into different time periods based on the frequency, and controlling to output different bio-electricity signals based on the space steering strategy configuration result in the different time periods. The multi-frequency cooperative control of different muscle groups is realized by constructing a frequency function mapping mechanism in combination with a space-oriented strategy and time partitioning, so that bio-electricity signal control is carried out according to the characteristics of a target tissue.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a multi-frequency bioelectric signal control method, device, equipment and storage medium. Background Art

[0002] Bioelectric signal stimulation technology is widely used in medical and rehabilitation scenarios such as neuromuscular rehabilitation, pain management, functional reconstruction, and neuroregulation.

[0003] However, existing multi-frequency bioelectrical stimulation methods still have numerous shortcomings. Most systems employ simple frequency superposition or parallel output modes, where multiple fixed-frequency signals are simply superimposed and output synchronously. This approach lacks effective temporal, spatial, or physiological differentiation between the individual frequency signals. This simple superposition approach can lead to interference between signals, harmonic accumulation, and even unnecessary physiological stress, thereby undermining the inherent advantages of multi-frequency control. Summary of the Invention

[0004] The embodiments of the present application provide a multi-frequency bioelectric signal control method, device, equipment and storage medium. By constructing a frequency function mapping mechanism, combining spatial guidance strategy and time partitioning, multi-frequency collaborative control of different muscle groups is achieved to control bioelectric signals according to the characteristics of the target tissue.

[0005] In a first aspect, an embodiment of the present application provides a multi-frequency bioelectric signal control method, comprising:

[0006] Acquiring target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies;

[0007] Based on the spatial distribution of target organizations, space-oriented strategy configuration is performed;

[0008] The control cycle is divided into different time periods based on the frequency, and different bioelectric signals are controlled to be output within the different time periods based on the spatial guidance strategy configuration results.

[0009] In one embodiment, obtaining target tissue parameters includes:

[0010] Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0011] In one embodiment, the method further comprises: constructing a mapping rule between tissue parameters and frequencies.

[0012] In one embodiment, the step of constructing a mapping rule between tissue parameters and frequencies includes:

[0013] Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

[0014] In one embodiment, the frequency prediction function is expressed as:

[0015] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0016] In one embodiment, configuring a spatial guidance strategy based on the spatial distribution of target tissues includes:

[0017] Select a target electrode group arranged according to a preset rule;

[0018] Based on the arrangement of the target electrode group, the current path direction and importance weight of the target channel are controlled.

[0019] In one embodiment, dividing the control period into different time periods based on the frequency includes:

[0020] Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

[0021] In a second aspect, an embodiment of the present application provides a multi-frequency bioelectric signal control device, comprising:

[0022] a mapping module, configured to obtain target tissue parameters and map the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies;

[0023] Configuration module, used to configure space-oriented strategies based on the spatial distribution of target organizations;

[0024] The control module is used to divide the control period into different time periods based on the frequency, and control the output of different bioelectric signals within the different time periods based on the configuration results of the spatial guidance strategy.

[0025] In one embodiment, the mapping module is specifically configured to:

[0026] Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0027] In one embodiment, the apparatus further includes: a construction module configured to construct a mapping rule between tissue parameters and frequencies.

[0028] In one embodiment, the building block is specifically configured to:

[0029] Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

[0030] In one embodiment, the frequency prediction function is expressed as:

[0031] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0032] In one embodiment, the configuration module includes:

[0033] A selection unit, configured to select a target electrode group arranged according to a preset rule;

[0034] A control unit is used to control the current path direction and importance weight of the target channel based on the arrangement of the target electrode group.

[0035] In one embodiment, the control module is specifically configured to:

[0036] Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

[0037] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0038] Memory and processing modules;

[0039] The memory is used to store computer programs;

[0040] The processing module is used to execute the computer program and implement the steps of the multi-frequency bioelectric signal control method of the first aspect when executing the computer program.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program;

[0042] When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the multi-frequency bioelectric signal control method of the first aspect as described above.

[0043] The embodiments of the present application provide a multi-frequency bioelectric signal control method, apparatus, device, and storage medium, wherein the multi-frequency bioelectric signal control method includes: obtaining target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies; configuring a spatial guidance strategy based on the spatial distribution of the target tissue; dividing the control period into different time periods based on the frequency, and controlling the output of different bioelectric signals based on the results of the spatial guidance strategy configuration within the different time periods. By constructing a frequency function mapping mechanism, combining the spatial guidance strategy with time partitioning, multi-frequency coordinated control of different muscle groups is achieved, so as to control bioelectric signals according to the characteristics of the target tissue. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flow chart of a multi-frequency bioelectric signal control method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a flow chart for configuring a spatial guidance strategy based on the spatial distribution of target tissues provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a multi-frequency bioelectric signal control device provided in an embodiment of the present application;

[0048] Figure 4 A schematic block diagram of a multi-frequency bioelectric signal control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0051] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] It should be further understood that the term "and / or" used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0053] The technical solution provided in this application will be described in detail below with reference to the accompanying drawings.

[0054] See also Figure 1 , Figure 1 This is a flow chart of the multi-frequency bioelectric signal control method provided in the embodiment of the present application. The multi-frequency bioelectric signal control method provided in the embodiment of the present application is implemented by a multi-frequency bioelectric signal control device. The embodiment of the present application does not impose any limitation on the multi-frequency bioelectric signal control device. Specifically, Figure 1 As shown, the multi-frequency bioelectric signal control method includes steps S101 to S104. The details are as follows:

[0055] S101: Acquire target tissue parameters, and map the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies.

[0056] In one embodiment, acquiring target tissue parameters includes: collecting characteristic parameters of different muscle groups or regions, wherein the characteristic parameters include nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0057] It should be noted that before the pre-constructed mapping rules between organizational parameters and frequencies, it also includes the step of constructing mapping rules between organizational parameters and frequencies, so that after obtaining the target organizational parameters, the target organizational parameters can be mapped to the corresponding frequency based on the constructed mapping rules.

[0058] In one embodiment, constructing the mapping rules between tissue parameters and frequencies includes: based on the collected tissue parameters, inputting the collected tissue parameters into a frequency prediction function, determining the regression coefficient of the frequency prediction function, and obtaining the mapping rules between the tissue parameters and frequencies.

[0059] In one embodiment, the frequency prediction function is expressed as:

[0060] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0061] A statistical regression algorithm is used for the above-mentioned frequency prediction function. According to the existing sample data (such as the pre-collected nerve ending density, muscle fiber characteristics and tissue response characteristics), the optimal regression coefficient is trained so that the frequency output by the frequency prediction function is as close as possible to the actual observed frequency. That is, when the error is minimized, the corresponding frequency prediction function is the mapping function between tissue parameters and frequency, and the mapping function is used as the mapping rule between tissue parameters and frequency.

[0062] Through this mapping rule, it is possible to adjust the stimulation frequency according to the state (parameters) of the target tissue instead of using a fixed frequency, which is more adaptable to the differences in the target tissue and improves the secondary effect.

[0063] S102: Based on the spatial distribution of the target tissue, configure a spatial guidance strategy.

[0064] See also Figure 2 As shown, Figure 2 This is a flow chart of configuring a spatial guidance strategy based on the spatial distribution of target tissues provided in the embodiment of the present application. Figure 2 It can be seen that in this application, based on the spatial distribution of the target tissue, the spatial guidance strategy configuration is performed, including: S1021 to S1022. The details are as follows:

[0065] S1021: Select a target electrode group arranged according to a preset rule.

[0066] The target electrode group is usually distributed according to the spatial position and size of the target tissue (such as nerves, muscles or specific lesion areas). Specifically, it can be based on the spatial distribution data of the target tissue, such as the spatial position of the target tissue obtained by imaging (MRI, ultrasound, CT). After obtaining the spatial position of the target tissue, the target electrode group is selected based on the distance between the target tissue and each electrode in the electrode array arranged according to the preset rules. Among them, the electrode array arranged according to the preset rules includes an electrode array arranged according to a square rule, an electrode array arranged according to a hexagonal rule, or an electrode array arranged according to a ring rule. When the distance between the center position of the target tissue and the electrode in the electrode array is less than a preset threshold, the electrode is selected as the target electrode. The target electrode group consists of target electrodes.

[0067] S1022: Based on the arrangement of the target electrode group, control the current path direction and importance weight of the target channel.

[0068] In the electrode pair operation, the current flows from one electrode (anode) to the other electrode (cathode), which is called the current path direction. The current path direction can guide the concentration of stimulation energy.

[0069] Specifically, an anode and cathode electrode are selected from the target electrode group. Based on their positions in the target tissue, the anode and cathode electrodes with the shortest paths are matched to form electrode pairs. The current paths output by each electrode pair pass through or are concentrated in the target tissue. Different electrode pairs form different channels, called target channels. Because the current intensities of different target channels are not necessarily the same, target channels with higher weights can be allocated more current, while target channels with lower weights can be allocated less current.

[0070] Assume there are N current paths and assign a weight w to each current path i ,and The corresponding weights can be assigned based on the distance from the electrode to the center of the target tissue, for example: Among them, w i represents the weight of the i-th current path, d i represents the distance from the center point of the i-th current path to the center of the target tissue, d j It represents the distance from the center point of the jth current path to the center of the target tissue. As can be seen from the above formula, the closer to the center, the closer d i The smaller the 1 / d i The larger the weight w is, the i The bigger.

[0071] After determining the weights of the current paths and target channels, the output current magnitude and flow direction of each target channel are adjusted. The direction and intensity distribution of each current path are precisely controlled to maximize the focus of the stimulation energy on the target tissue area, thereby enhancing the stimulation effect.

[0072] S103: Dividing the control period into different time periods based on the frequency, and controlling the output of different bioelectric signals based on the spatial guidance strategy configuration results within the different time periods.

[0073] In one embodiment, dividing the control period into different time periods based on the frequency includes: determining a least common multiple period T as the control period based on the frequency, and dividing the control period into a plurality of time periods of equal or unequal lengths within the control period.

[0074] It should be noted that in practical applications, different target tissues typically correspond to different frequencies, or periods. However, to coordinate and simultaneously stimulate all target tissues, a common duration, T, must be found. This duration, T, must be an integer multiple of the period for each target tissue, known as the least common multiple period.

[0075] In different time periods, according to the electrode group, current direction, and weight configured by the previous spatial guidance strategy, different electrode pairs are activated to stimulate different target tissues. For example: Time period 1: mainly stimulate target tissue A, the channel is set to electrode group 1, the current direction is A to B, and the current intensity is 0.5mA; Time period 2: mainly stimulate target tissue B, the channel is set to electrode group 2, the current direction is C to D, and the intensity is 0.8mA; Time period 3: switch back to area A+B combined stimulation, with multiple channels in parallel. Not only can precise stimulation be achieved in space, but different stimulation modes can also be dynamically switched in time, taking into account multiple target tissues. And it can flexibly adjust strategies according to the responses of different target tissues.

[0076] Through the above analysis, it can be seen that the multi-frequency bioelectric signal control method provided by the embodiment of the present application includes: obtaining target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies; configuring a spatial guidance strategy based on the spatial distribution of the target tissue; dividing the control period into different time periods based on the frequency, and controlling the output of different bioelectric signals based on the results of the spatial guidance strategy configuration within the different time periods. By constructing a frequency function mapping mechanism, combining the spatial guidance strategy with time partitioning, multi-frequency coordinated control of different muscle groups can be achieved to control bioelectric signals according to the characteristics of the target tissue.

[0077] See also Figure 3 As shown, Figure 3This is a schematic diagram of the structure of the multi-frequency bioelectric signal control device provided in the embodiment of the present application. Figure 3 It can be seen that the multi-frequency bioelectric signal control device 300 provided in the embodiment of the present application includes:

[0078] A mapping module 310 is configured to obtain target tissue parameters and map the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies;

[0079] A configuration module 320 is used to configure a spatial guidance strategy based on the spatial distribution of target tissues;

[0080] The control module 330 is configured to divide the control period into different time periods based on the frequency, and control the output of different bioelectric signals within the different time periods based on the spatial guidance strategy configuration results.

[0081] In one embodiment, the mapping module 310 is specifically configured to:

[0082] Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0083] In one embodiment, the apparatus 300 further includes: a construction module configured to construct a mapping rule between tissue parameters and frequencies.

[0084] In one embodiment, the building block is specifically configured to:

[0085] Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

[0086] In one embodiment, the frequency prediction function is expressed as:

[0087] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0088] In one embodiment, the configuration module 320 includes:

[0089] A selection unit, configured to select a target electrode group arranged according to a preset rule;

[0090] A control unit is used to control the current path direction and importance weight of the target channel based on the arrangement of the target electrode group.

[0091] In one embodiment, the control module 330 is specifically configured to:

[0092] Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

[0093] It should be noted that the specific implementation process of each of the above modules or units can refer to the specific implementation process of each step in the previous method embodiment, which will not be repeated here.

[0094] See also Figure 4 As shown, Figure 4 A schematic block diagram of a multi-frequency bioelectric signal control device provided in an embodiment of the present application.

[0095] Exemplarily, the multi-frequency bioelectric signal control device 400 includes a processing module 401 and a memory 402 .

[0096] Exemplarily, the processing module 401 and the memory 402 are connected via a bus 403 , which is, for example, an I 2 C (Inter-integrated Circuit) bus.

[0097] Specifically, the processing module 401 may be a micro-controller unit (MCU), a central processing unit (CPU), or a digital signal processing module (DSP).

[0098] Specifically, the memory 402 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0099] The processing module 401 is used to run the computer program stored in the memory 402 and implement the steps of the multi-frequency bioelectric signal control method when executing the computer program.

[0100] Exemplarily, the processing module 401 is configured to run a computer program stored in the memory 402 and implement the following steps when executing the computer program:

[0101] Acquiring target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies;

[0102] Based on the spatial distribution of target organizations, space-oriented strategy configuration is performed;

[0103] The control cycle is divided into different time periods based on the frequency, and different bioelectric signals are controlled to be output within the different time periods based on the spatial guidance strategy configuration results.

[0104] In one embodiment, obtaining target tissue parameters includes:

[0105] Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0106] In one embodiment, the method further comprises: constructing a mapping rule between tissue parameters and frequencies.

[0107] In one embodiment, the step of constructing a mapping rule between tissue parameters and frequencies includes:

[0108] Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

[0109] In one embodiment, the frequency prediction function is expressed as:

[0110] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0111] In one embodiment, configuring a spatial guidance strategy based on the spatial distribution of target tissues includes:

[0112] Select a target electrode group arranged according to a preset rule;

[0113] Based on the arrangement of the target electrode group, the current path direction and importance weight of the target channel are controlled.

[0114] In one embodiment, dividing the control period into different time periods based on the frequency includes:

[0115] Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

[0116] The specific principles and implementation methods of the multi-frequency bioelectric signal control device provided in the embodiment of the present application are similar to those of the multi-frequency bioelectric signal control method in the aforementioned embodiment, and will not be repeated here.

[0117] The present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processing module, the processing module implements the following steps:

[0118] Acquiring target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies;

[0119] Based on the spatial distribution of target organizations, space-oriented strategy configuration is performed;

[0120] The control cycle is divided into different time periods based on the frequency, and different bioelectric signals are controlled to be output within the different time periods based on the spatial guidance strategy configuration results.

[0121] In one embodiment, obtaining target tissue parameters includes:

[0122] Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

[0123] In one embodiment, the method further comprises: constructing a mapping rule between tissue parameters and frequencies.

[0124] In one embodiment, the step of constructing a mapping rule between tissue parameters and frequencies includes:

[0125] Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

[0126] In one embodiment, the frequency prediction function is expressed as:

[0127] Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N drepresents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

[0128] In one embodiment, configuring a spatial guidance strategy based on the spatial distribution of target tissues includes:

[0129] Select a target electrode group arranged according to a preset rule;

[0130] Based on the arrangement of the target electrode group, the current path direction and importance weight of the target channel are controlled.

[0131] In one embodiment, dividing the control period into different time periods based on the frequency includes:

[0132] Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

[0133] The computer-readable storage medium may be an internal storage unit of the multi-frequency bioelectric signal control device in the aforementioned embodiment, such as a hard disk or memory of the multi-frequency bioelectric signal control device. The computer-readable storage medium may also be an external storage device of the multi-frequency bioelectric signal control device, such as a plug-in hard disk equipped with the multi-frequency bioelectric signal control device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0134] It should be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application.

[0135] It will also be understood that the term "and / or" as used in this application and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0136] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A multi-frequency bioelectric signal control method, characterized in that: include: Acquiring target tissue parameters, and mapping the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies; Based on the spatial distribution of target organizations, space-oriented strategy configuration is performed; The control cycle is divided into different time periods based on the frequency, and different bioelectric signals are controlled to be output within the different time periods based on the spatial guidance strategy configuration results.

2. The multi-frequency bioelectric signal control according to claim 1, characterized in that: The obtaining of target tissue parameters includes: Characteristic parameters of different muscle groups or regions are collected, including nerve ending density, muscle fiber characteristics, and physiological response characteristics.

3. The multi-frequency bioelectric signal control method according to claim 2, characterized in that: The method further includes: constructing a mapping rule between tissue parameters and frequencies.

4. The multi-frequency bioelectric signal control method according to claim 3, characterized in that: The mapping rule between the tissue parameters and the frequency is constructed, including: Based on the collected tissue parameters, the collected tissue parameters are input into a frequency prediction function, regression coefficients are determined for the frequency prediction function, and a mapping rule between the tissue parameters and frequencies is obtained.

5. The multi-frequency bioelectric signal control method according to claim 4, characterized in that: The frequency prediction function is expressed as: Where f represents the target stimulation frequency of the channel to be controlled; a1, a2, a3, and b represent the regression coefficients of the frequency prediction function, which are used to minimize the error between the predicted target secondary frequency and the actual optimal frequency; N d represents the nerve ending density vector obtained in the selected time interval; F r represents the muscle fiber feature vector obtained in the selected time interval; It represents the integral value of tissue response characteristics within a selected time interval and is used to measure the overall physiological response intensity.

6. The multi-frequency bioelectric signal control method according to claim 1, characterized in that: The spatial guidance strategy configuration based on the spatial distribution of the target tissue includes: Select a target electrode group arranged according to a preset rule; Based on the arrangement of the target electrode group, the current path direction and importance weight of the target channel are controlled.

7. The multi-frequency bioelectric signal control method according to claim 6, characterized in that: The dividing the control period into different time periods based on the frequency includes: Based on the frequency, a least common multiple period T is determined as a control period, and the control period is divided into a number of time periods of equal or unequal lengths.

8. A multi-frequency bioelectric signal control device, characterized in that: include: a mapping module, configured to obtain target tissue parameters and map the target tissue parameters to corresponding frequencies based on pre-established mapping rules between tissue parameters and frequencies; Configuration module, used to configure space-oriented strategies based on the spatial distribution of target organizations; The control module is used to divide the control period into different time periods based on the frequency, and control the output of different bioelectric signals within the different time periods based on the configuration results of the spatial guidance strategy.

9. An electronic device, characterized in that: include: Memory and processing modules; The memory is used to store computer programs; The processing module is used to execute the computer program and implement the steps of the multi-frequency bioelectric signal control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program; When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the multi-frequency bioelectric signal control method according to any one of claims 1 to 7.

Images