Flexible electrophysiological probe based on muscle vein deformation-myoelectricity matching and preparation method thereof

Abstract

The invention discloses a flexible electrophysiological probe based on muscle vein deformation-myoelectricity matching and a preparation method thereof.The probe comprises an ultrasonic module responsible for collecting muscle ultrasonic data of the lower limbs of a patient in real time, and a myoelectricity sensing module responsible for collecting myoelectricity sensing data of the lower limbs of the patient in real time; the local data processing unit is responsible for carrying out myoelectricity-ultrasonic data fusion analysis, obtaining a muscle vein deformation-myoelectricity characteristic real-time matching result and deciding a gradient electrical stimulation scheme according to the result; and the electrical stimulation module is responsible for realizing electrophysiological stimulation intervention on the lower limbs of the patient according to the gradient electrical stimulation scheme. By integrating ultrasonic imaging, myoelectricity monitoring and electrophysiological stimulation functions on the flexible base material, synchronous acquisition of muscle ultrasonic data and myoelectricity induction data, dynamic calculation of a muscle vein deformation-myoelectricity characteristic real-time matching result and dynamic closed-loop regulation and control of electrophysiological stimulation intervention are realized; and non-inductive wearing of the skin in a motion state is realized, and motion artifacts and data distortion are avoided.

Description

Technical Field

[0001] This invention belongs to the field of sports medicine technology, specifically relating to a flexible electrophysiological probe based on muscle vein deformation-electromyography matching and its preparation method. Background Technology

[0002] Currently, in assessing lower limb muscle pump function or in the process of sports injury early warning and rehabilitation intervention, it is usually necessary to use corresponding probes for scanning. However, existing probes have the following limitations: 1. Problem of single monitoring dimension: Traditional equipment can only independently collect electromyographic signals or muscle deformation data, and lacks multimodal synchronous monitoring of the coupling relationship between venous return efficiency and electromyographic activity; 2. Limitations of rigid devices: Existing ultrasound probes and electromyography electrodes are mostly rigid structures, which are difficult to conform to the curved surface of the human lower limb, resulting in motion artifacts and data distortion; 3. Lack of closed-loop regulation: The electromyography signal analysis system is separated from the electrical stimulation intervention module, making it impossible to achieve adaptive regulation based on real-time physiological feedback. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a flexible electrophysiological probe based on muscle-venous deformation-electromyography matching and its preparation method. The aim is to quantify the relationship between the continuous blood pumping capacity of lower limb muscles under various exercise states and their electromyographic changes, providing a new technical means for sports medicine research and clinical applications.

[0004] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: A flexible electrophysiological probe based on muscle vein deformation-electromyography matching includes: The ultrasound module contains multiple electromyography sensing units, which are responsible for collecting real-time ultrasound data of the patient's lower limb muscles by emitting broadband ultrasound waves, thereby dynamically capturing the muscle contraction deformation and changes in the diameter of the veins in the patient's lower limbs. The electromyography (EMG) sensing module contains multiple electrical stimulation units and is responsible for collecting EMG data of the patient's lower limbs in real time, thereby simultaneously acquiring the EMG signals and local muscle fiber stress distribution on the surface of the patient's lower limbs. The number of ultrasound units is equal to the number of electromyography (EMG) sensing units. Each ultrasound unit is paired with a corresponding EMG sensing unit to form an ultrasound-EMG matching unit. The local data processing unit is responsible for performing electromyography-ultrasound data fusion analysis on the collected muscle ultrasound data and electromyography induction data to obtain the real-time matching results of muscle vein deformation-electromyography features, the evaluation results of the impact of overall contraction / extension of lower limb muscle veins on volume, and the evaluation results of lower limb muscle infusion efficiency. Based on the obtained real-time matching results of muscle vein deformation-electromyography features, the unit makes gradient electrical stimulation scheme decisions. The electrical stimulation module contains multiple electrical stimulation units, which are responsible for outputting stimulation currents of corresponding intensities according to the determined gradient electrical stimulation scheme, so as to realize electrophysiological stimulation intervention on the patient's lower limbs. The number of the electrical stimulation units is equal to the number of the electromyographic sensing units; A thin, flexible substrate is used to integrate the ultrasound module, the electromyography sensing module, the electrical stimulation module, and the local data processing unit into a flexible, heterogeneous integrated device, thereby enabling it to conformally adhere to the surface of the patient's lower limb.

[0005] Furthermore, the local data processing unit achieves electromyography-ultrasound data fusion analysis through a built-in embedded AI chip, specifically using the following method: First, the obtained muscle ultrasound data and electromyography (EMG) data are synchronized using a data synchronization engine. Then, muscle vein volume features and EMG features are extracted from the synchronized muscle ultrasound data and EMG data, respectively. Next, the extracted muscle vein volume features and EMG features are sent to a spatiotemporal alignment matrix for spatiotemporal data synchronization and calibration. Then, the spatiotemporally synchronized and calibrated muscle vein volume features and EMG features are sent to a multimodal EMG-ultrasound matching model. Finally, the multimodal EMG-ultrasound matching model performs dynamic matching calculations and outputs the real-time matching results of muscle vein deformation and EMG features.

[0006] Furthermore, the extraction of the muscle vein volume features includes calculating the amplitude of movement of the lower limb muscle veins in the short axis direction and calculating the volume change of the lower limb muscle veins in the short axis direction. The specific calculation process is as follows: 1) Calculate the amplitude of movement of the lower limb muscles and veins along the short axis using the following formula: (1); In formula (1), S i The minor axis deformation for each acquisition point i; Δbᵢ is the deformation of the short semi-axis of the i-th section; 2) Calculate the volume change of the lower limb muscles and veins along the short axis using the following formula: (3); In formula (3), a0 is the average semi-major axis of all elliptical cross sections, in centimeters; S i denoted as , where is the minor axis movement amplitude of the i-th sampling point, i.e., the change in the minor semi-axis; n is the total number of sampling points; and d is the distance between adjacent sampling points, in centimeters.

[0007] Furthermore, the local data processing unit can directly assess the impact of overall contraction / extension of the lower limb muscles and veins on volume by utilizing the calculated amplitude of movement of the lower limb muscles and veins in the short axis direction and the volume change of the lower limb muscles and veins in the short axis direction. And / or, the local data processing unit can use the calculated amplitude of movement of the lower limb muscle veins in the short axis direction and the volume change of the lower limb muscle veins in the short axis direction, and combine them with parameters including cadence and stride length, to establish a quantitative model of muscle pumping efficiency and exercise load, and evaluate the lower limb muscle pumping efficiency.

[0008] Furthermore, the dynamic matching calculation process is as follows: 1) Preoperative preparation: Resting state monitoring: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the baseline values ​​of the lower limb venous deformation volume V0 and the lower limb E0 EMG signals were measured at rest. Maximum contraction test: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the muscle deformation rate Dmax and Emax of the lower limb muscles during active contraction were recorded; Baseline database establishment: A baseline database is established using the measured baseline values ​​V0 of the lower limb venous deformation volume and E0 of the lower limb electromyography signal, as well as the recorded muscle deformation rate Dmax and electromyography signal Emax during active contraction of the lower limb muscles. Key parameter storage: Save the baseline value V0 of the measured lower limb venous deformation volume, i.e., the venous volume in the supine position; save the baseline value E0 of the measured lower limb electromyography signal, i.e., the resting electromyography amplitude; save the recorded muscle deformation rate Dmax during active lower limb muscle contraction, i.e., the maximum deformation rate; save the recorded electromyography signal Emax during active lower limb muscle contraction, i.e., the maximum contractile electromyography. 2) Postoperative real-time monitoring: Dual-modal data acquisition: The ultrasound module and the electromyography sensing module respectively measure the recorded value Vt of the venous deformation volume of the lower limb of the patient after surgery and the recorded value Et of the electrical signal intensity of the muscle of the lower limb of the patient after surgery. Dynamic matching calculation: The local data processing unit uses the recorded value Vt of the postoperative lower limb venous deformation volume and the recorded value E0 of the postoperative lower limb electromyography signal, along with the baseline database, to perform dynamic matching calculation. The calculation formula is as follows: Match degree M = (Vt / V0) × (Et / E0); Triggering condition: When the matching degree M > 0.5, the gradient strength decision phase is initiated.

[0009] Furthermore, the gradient electrical stimulation scheme includes at least an initial electrical stimulation intensity and three levels of gradient electrical stimulation intensity. Specifically, the initial electrical stimulation intensity (50%) is set to a current intensity that enables the lower limb electromyographic amplitude to reach 50% of the electromyographic signal Emax during active lower limb muscle contraction; the first level of electrical stimulation intensity (50%-70%) is set to a current intensity that increases the lower limb blood flow velocity by 20%; the second level of electrical stimulation intensity (70%-85%) is set to a current intensity that enables the lower limb electromyographic amplitude to reach 80% of the electromyographic signal Emax during active lower limb muscle contraction; and the third level of electrical stimulation intensity (85%-100%) is set to a current intensity that enables the lower limb muscle deformation rate to reach more than 80% of the muscle deformation rate Dmax during active lower limb muscle contraction.

[0010] Furthermore, the current intensity of each of the electrical stimulation units is adjusted by a programmable multi-channel current generator, which is integrated on the thin flexible substrate and supports bidirectional 0.1-100mA pulse width modulation. The electrical stimulation intensity is dynamically adjusted according to the impedance matching algorithm to achieve precise control and adaptive adjustment of the electrical stimulation parameters.

[0011] Furthermore, the electromyography sensing unit and the electrical stimulation unit are the same electrode unit that has both electromyography sensing and electrical stimulation functions. The receiving of electrical signals corresponds to electromyography signal induction, and the release of electrical signals corresponds to electrical stimulation triggering. Each ultrasound unit and its corresponding electrode unit are paired to form an ultrasound-electromyography matching unit.

[0012] Furthermore, the ultrasound-electromyography matching unit can be attached to the surface of the patient's lower limb muscles in an array-like distribution structure. The array-like distribution structure is as follows: all the ultrasound-electromyography matching units are arranged in an array on the same large-size thin flexible material. Specifically, all the ultrasound units are arranged in an array on one side surface of the large-size thin flexible material to form an ultrasound array, and all the electromyography sensing units are arranged in an array on the other side surface of the large-size thin flexible material to form an electromyography sensing array. Moreover, the ultrasound array and the electromyography sensing array are staggered on both sides of the large-size thin flexible substrate. That is, the array elements of the electromyography sensing array are located in the gaps between the array elements of the ultrasound array. The array elements of the electromyography sensing array and the array elements of the ultrasound array are staggered and do not overlap. A pair of ultrasound units and electromyography sensing units that are close in position are paired to form an ultrasound-electromyography matching unit.

[0013] Furthermore, the ultrasound-electromyography matching unit can be applied to the surface of the patient's lower limb muscles in a distributed structure. The distributed structure is as follows: all the ultrasound-electromyography matching units are dispersed in groups of one on several small-sized thin flexible materials. Specifically, several ultrasound units are evenly arranged on one side surface of each small-sized thin flexible material, and several electromyography sensing units are evenly arranged on the other side surface of each small-sized thin flexible material. The number of ultrasound units and electromyography sensing units on the same small-sized thin flexible material is equal. Moreover, the ultrasound units and electromyography sensing units on the same small-sized thin flexible material are staggered and do not overlap. A pair of ultrasound units and electromyography sensing units in close proximity are paired to form an ultrasound-electromyography matching unit.

[0014] A method for fabricating a flexible electrophysiological probe based on muscle vein deformation-electromyography matching, as described above, includes the following steps: Step 1: Pretreatment of the thin flexible substrate: A thin flexible substrate is made of polydimethylsiloxane or silicone composite material. First, several ultrasonic unit cavities are arranged in an array on one side surface of the thin flexible substrate, and several electrode unit cavities are arranged in an array on the other side surface of the thin flexible substrate. The number of ultrasonic unit cavities and electrode unit cavities are equal and their positions do not overlap. Then, a micron-scale protrusion structure array is formed on the bottom surface of each electrode unit cavity using a molding process, and then activated with O2 plasma. Step 2, Construction of the ultrasound array: Using inkjet printing technology, a piezoelectric ceramic / polymer composite film capable of emitting 0.1-5MHz M-mode broadband ultrasonic waves is deposited in the cavity of each ultrasonic unit. Each piezoelectric ceramic / polymer composite film serves as an ultrasonic unit, and all ultrasonic units form an ultrasonic array on one side surface of the thin flexible substrate. Step 3: Construction of the electromyography / electrical stimulation array: First, CuNWs / PEDOT hybrid conductive ink is selected as the raw material. Aerosol jet deposition or inkjet printing technology is used to deposit or print an electromyography (EMG) sensing / electrostimulation electrode within each electrode cavity. During the deposition or printing process, an asymmetric micron-sized pore or mesh array is formed on the bottom surface of each EMG sensing / electrostimulation electrode. This asymmetric micron-sized pore or mesh array and the micron-sized protrusion array form a mechanical interlocking structure, thereby enhancing the adhesion of the EMG sensing / electrostimulation electrode within the electrode cavity. Each EMG sensing / electrostimulation electrode serves as an electrode unit, and all electrode units form an electrode array on the other side of the thin flexible substrate, i.e., an EMG sensing / electrostimulation array. Then, an antioxidant layer is coated on the surface of the electrode array, followed by photocuring and post-processing. At this time, the ultrasonic array and the electrode array are arranged in a staggered manner on both sides of the thin flexible substrate. That is, the electrode unit of the electrode array is located in the gap between the ultrasonic units of the ultrasonic array. The electrode unit of the electrode array and the ultrasonic unit of the ultrasonic array are staggered and do not overlap. A pair of ultrasonic units and electrode units with close positions are paired to form an ultrasonic-electromyography matching unit. Step 4: Construction of the flexible circuit board: First, the designed electromyography-ultrasound data fusion algorithm and gradient electrical stimulation scheme are stored in the local data processing unit with an embedded AI chip. Then, the local data processing unit and the programmable multi-channel current generator are installed on a flexible circuit board. Next, the flexible circuit board is attached to the surface of one end of the thin flexible substrate. Finally, a communication connection is established between the flexible circuit board and the constructed ultrasound array and electromyography sensing / electrical stimulation array via Bluetooth 5.2 / Wi-Fi, thereby realizing a flexible patch probe with a three-in-one function of signal acquisition, processing and stimulation output.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Multimodal sensing fusion: This invention is the first to integrate ultrasound motion monitoring, electromyography signal acquisition and electrophysiological stimulation into a single flexible device. It has the functions of simultaneous acquisition of muscle ultrasound data and electromyography data and real-time intervention of lower limb muscle electrical stimulation. It breaks through the technical bottleneck of functional separation of existing devices and is expected to provide new technical means for sports medicine research and clinical application.

[0016] 2. Ultra-thin conformal design: This invention uses micro-nano fabrication technology to integrate ultrasonic arrays, electromyographic sensing arrays, and electrical stimulation arrays onto the same thin flexible substrate, producing heterogeneous integrated devices with a thickness of <500μm and an elongation of ≥200%. This not only enables seamless wearing under the skin during movement, but also allows the ultrasonic array and electromyographic sensing array to perfectly conform to the curved surface of the human lower limb, thereby avoiding motion artifacts and data distortion.

[0017] 3. Dynamic closed-loop control: This invention can perform local real-time data fusion processing on the obtained muscle contraction deformation and venous diameter changes, as well as electromyographic signals and local muscle fiber stress distribution, thereby obtaining real-time matching results of muscle and vein deformation-electromyographic features. Based on these real-time matching results, it can automatically trigger gradient electrical stimulation programs to improve the efficiency of lower limb venous return by up to 30%.

[0018] 4. Applicable to multiple application scenarios: By integrating ultrasound imaging, electromyography monitoring and electrophysiological stimulation functions, this invention can quantify the relationship between the continuous blood pumping capacity of lower limb muscles under various exercise states and their electromyographic changes. It can realize dynamic assessment of lower limb muscle blood pumping function, as well as sports injury early warning and rehabilitation intervention. It is also expected to be used for monitoring the blood pumping efficiency of quadriceps femoris muscles during athletes' squats and sprints, early warning of the risk of lower limb venous diseases in high-risk subclinical populations, including lower limb venous thrombosis and lower limb varicose veins, and to improve lower limb muscle atrophy in stroke patients.

[0019] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall structure of the flexible ultrasound electrophysiology probe (array type) of the present invention; Figure 2 This is a schematic diagram of the structure of the flexible ultrasound electrophysiology probe (array type) of the present invention after the ultrasound units are deposited in an array on a thin flexible substrate. Figure 3 This is a schematic diagram of an ultrasound-electromyography matching unit of the flexible ultrasound electrophysiology probe (array / distributed) of the present invention. Figure 4This is an electron microscope schematic diagram of the micron-scale protrusion structure array of the flexible ultrasound electrophysiology probe (array type / dispersed type) of the present invention. Figure 5 This is an electron microscope schematic diagram of the asymmetric micron-scale holes or grid array of the flexible ultrasound electrophysiology probe (array type / distributed type) of the present invention. Figure 6 This is a schematic diagram illustrating the working principle of the flexible ultrasound electrophysiology probe (array / distributed type) of the present invention. Figure 7 This is a schematic diagram illustrating the wearing method of the flexible ultrasound electrophysiology probe (array type) of the present invention; Figure 8 This is a flowchart illustrating the steps of the flexible ultrasound electrophysiology probe (array / distributed) of the present invention from multimodal data acquisition to real-time matching results of muscle deformation and electromyographic features. Figure 9 This is a schematic diagram of the optimal sampling line (short axis view) for selecting veins using a single flexible array element according to the present invention. Figure 10 This is a schematic diagram of the spacing between multiple flexible array elements and the scanning plane (major axis view) of the present invention; Figure 11 This is a schematic diagram illustrating the steps of the dynamic matching process of the flexible ultrasound electrophysiology probe (array / distributed) of the present invention. Figure 12 This is a schematic diagram of the gradient electrical stimulation scheme and electrophysiological stimulation intervention strategy of the flexible ultrasound electrophysiological probe (array / distributed) of the present invention. Figure 13 This is a schematic diagram of the single-module structure of the flexible ultrasound electrophysiology probe (distributed type) of the present invention; Figure 14 This is a schematic diagram illustrating the wearing method of the flexible ultrasound electrophysiology probe (distributed type) of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings to provide a clearer understanding of the invention's purpose, features, and advantages. It should be understood that the embodiments shown in the drawings are not intended to limit the scope of the invention, but are merely illustrative of the essential spirit of the invention's technical solutions. Furthermore, the technical features involved in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other. Example

[0022] See Figure 1 As shown, this embodiment provides a flexible electrophysiological probe (array type) based on muscle vein deformation-electromyography matching, which mainly includes: a thin flexible substrate 1, and an ultrasound array, an electrode array and a local data processing unit 4 integrated on the thin flexible substrate 1.

[0023] The thin flexible substrate 1 is responsible for integrating the ultrasound array, the electrode array and the local data processing unit 4 into a flexible heterogeneous integrated device, thereby achieving conformal adhesion to the patient's lower limb surface.

[0024] The ultrasound array is responsible for transmitting broadband ultrasound waves to collect real-time ultrasound data of the patient's lower limb muscles, thereby dynamically capturing the muscle contraction deformation and changes in the diameter of the veins in the patient's lower limbs.

[0025] The electrode array serves as both an electromyography (EMG) sensing array and an electrical stimulation array, combining both functions. As an EMG sensing array, it is responsible for real-time acquisition of EMG data from the patient's lower limbs, thereby simultaneously obtaining the sEMG signals and local muscle fiber stress distribution on the patient's lower limb surface. As an electrical stimulation array, it is responsible for outputting a stimulation current of corresponding intensity according to the determined gradient electrical stimulation scheme, achieving electrophysiological stimulation intervention on the patient's lower limbs.

[0026] The local data processing unit 4 is responsible for performing electromyography-ultrasound data fusion analysis on the collected muscle ultrasound data and electromyography induction data to obtain the real-time matching results of muscle vein deformation-electromyography characteristics, the evaluation results of the impact of overall contraction / extension of lower limb muscle veins on volume, and the evaluation results of lower limb muscle infusion efficiency. Based on the obtained real-time matching results of muscle vein deformation-electromyography characteristics, it makes decisions on gradient electrical stimulation schemes.

[0027] Preferably, the thin flexible substrate 1 is made of polydimethylsiloxane (PDMS) or silicone composite material. The front side of the thin flexible substrate 1 has several ultrasonic unit cavities arranged in an array, and the back side of the thin flexible substrate 1 has several electrode unit cavities arranged in an array. The number of ultrasonic unit cavities and electrode unit cavities are equal, and their positions do not overlap.

[0028] Preferably, the ultrasonic array comprises a plurality of ultrasonic units 2, each of which is a piezoelectric ceramic / polymer composite film deposited within the cavity of the corresponding ultrasonic unit using inkjet printing technology. See also Figure 2 As shown, all of the ultrasonic units 2 form the ultrasonic array on the front side of the thin flexible substrate 1.

[0029] Preferably, the ultrasonic array can emit M-mode broadband ultrasound waves of 0.1-5MHz to adapt to the monitoring of lower limb muscle and blood vessel deformation under different movement states.

[0030] Preferably, the electrode array comprises a plurality of electrode units 3, each electrode unit 3 being a composite of a nano-conductive material and an elastic matrix, and deposited or printed in the corresponding electrode unit cavity using a digital printing process. All the electrode units 3 form the electrode array on the front side of the thin flexible substrate 1.

[0031] Preferably, the nanoconductive material is selected from at least one of CuNWs (copper nanowires), MXene (transition metal carbides), or carbon nanotubes, with CuNWs being the most preferred.

[0032] Preferably, the elastic matrix is ​​selected from PEDOT conductive polymer.

[0033] Preferably, the diameter of CuNWs is 60-120nm, more preferably 80-100nm, the aspect ratio is >200, the surface coating rate of PEDOT is 70-90%, and the mass ratio of CuNWs to PEDOT is (3-8):(2-5), preferably 4:3.

[0034] Preferably, the thickness of the electrode array is controlled between 5-15 μm, and more preferably 12 ± 2 μm.

[0035] Preferably, the surface roughness Ra of the electrode array is 0.5-1.5 μm.

[0036] See Figure 1 As shown, the ultrasonic array and the electrode array are arranged in a staggered manner on both sides of the thin flexible substrate. That is, the electrode units of the electrode array are located in the gaps between the ultrasonic units of the ultrasonic array, and the electrode units of the electrode array and the ultrasonic units of the ultrasonic array are offset from each other and do not overlap. See also... Figure 3 As shown, a pair of ultrasound units and electrode units located close to each other form an ultrasound-electromyography matching unit, which together with the thin flexible substrate 1 forms a "sandwich" structure.

[0037] Preferably, the ultrasonic array uses 4×8 or 8×8 ultrasonic units 2, and correspondingly, the electrode array uses 4×8 or 8×8 electrode units 3.

[0038] Preferably, a layer of micron-scale protrusion array is formed on the bottom surface of each electrode unit cavity using a molding process (see...). Figure 4 As shown), simultaneously, during the deposition or printing process, the bottom surface of each electrode unit 3 forms a layer of asymmetric micron-sized pores or a mesh array (see...). Figure 5(As shown). When the electrode unit 3 is disposed in the electrode unit cavity, the asymmetric micron-sized holes or grid array can form a mechanical interlocking structure with the micron-sized protrusion structure array, thereby enhancing the adhesion of the electrode unit 3 in the electrode cavity.

[0039] Preferably, the height of each protrusion in the micron-scale protrusion structure array is 5-20 μm, and the spacing between two adjacent protrusion structures is 30-100 μm.

[0040] Preferably, the shape of the holes or grids in the asymmetric micron-sized pore or grid array can be designed as a plum blossom-shaped fractal pore structure with a pore size of 0-180μm, a porosity of 25-40%, and a pore wall width of 10-30μm.

[0041] It should be noted that the asymmetric micron-sized holes or grid array is a hollow structure inherent in the electrode unit 3 itself, rather than being formed by setting the micron-sized protrusion array on the surface of the thin flexible substrate 1. This asymmetric micron-sized holes or grid array can improve charge transfer efficiency.

[0042] Preferably, the local data processing unit 4 is disposed on the surface of one end of the thin flexible substrate 1 via a flexible circuit board. The flexible circuit board is also provided with a programmable multi-channel current generator responsible for adjusting the current intensity of the electrical stimulation array.

[0043] Preferably, the programmable multichannel current generator is based on an H-bridge topology, supports bidirectional 0.1-100mA pulse width modulation, and can dynamically adjust the electrical stimulation intensity of the electrical stimulation array according to an impedance matching algorithm, thereby achieving precise control and adaptive adjustment of the electrical stimulation parameters.

[0044] The programmable multi-channel current generator has the following adjustable pulse parameter range: Frequency: 10-200Hz; Pulse width: 100-500μs; Intensity: 0.1-100mA (resolution 0.1mA).

[0045] The thin, flexible substrate 1, after integrating the ultrasonic array, the electrode array, and the local data processing unit 4, forms a flexible heterogeneous integrated device with an overall thickness not exceeding 500 μm and an elongation ≥200%. Therefore, this flexible electrophysiological probe of the present invention can not only achieve synchronous acquisition of muscle ultrasound data and electromyographic sensing data, dynamic calculation of the real-time matching results of muscle vein deformation and electromyographic characteristics, and adaptive control of electrophysiological stimulation intervention, but also achieve imperceptible wear under exercise.

[0046] See Figure 6 As shown, the working principle of the flexible electrophysiological probe based on muscle vein deformation-electromyography matching in this invention basically includes four stages: sensing and recording, dynamic matching, gradient intensity decision-making, and electrical stimulation. The specific process is as follows: Step 1, Sensing and Recording Stage: See Figure 7 As shown, the flexible ultrasound electrophysiological probe of the present invention can be placed on a flexible bandage and then worn on the patient's lower limb in a manner that conforms to the skin's curvature.

[0047] The ultrasound array collects real-time ultrasound data of the patient's lower limb muscles, thereby dynamically capturing the muscle contraction deformation and changes in the diameter of the veins in the patient's lower limbs.

[0048] Simultaneously, the electromyography sensor array collects electromyography data of the patient's lower limbs in real time, thereby synchronously acquiring the electromyography signal (sEMG) on the surface of the patient's lower limbs and the local muscle fiber stress distribution.

[0049] The obtained muscle ultrasound data and electromyography data are processed by the signal conditioning circuit and then transmitted to the data processing unit 4 on the thin flexible substrate 1 via Bluetooth 5.2 / Wi-Fi.

[0050] Step 2, Dynamic Matching Stage: See Figure 8 As shown, the data processing unit 4 performs electromyography-ultrasound data fusion analysis on the received muscle ultrasound data and electromyography induction data to obtain real-time matching results of muscle vein deformation-electromyography features.

[0051] The local data processing unit 4 uses a built-in embedded AI chip to perform electromyography-ultrasound data fusion analysis. The specific method is as follows: First, the obtained muscle ultrasound data and electromyography (EMG) data are synchronized using a data synchronization engine. Then, muscle vein volume features and EMG features are extracted from the synchronized muscle ultrasound data and EMG data, respectively. Next, the extracted muscle vein volume features and EMG features are sent to a spatiotemporal alignment matrix for spatiotemporal data synchronization and calibration. Then, the spatiotemporally synchronized and calibrated muscle vein volume features and EMG features are sent to a multimodal EMG-ultrasound matching model. Finally, the multimodal EMG-ultrasound matching model performs dynamic matching calculations and outputs the real-time matching results of muscle vein deformation and EMG features.

[0052] The extraction of the muscle vein volume characteristics includes calculating the amplitude of movement of the lower limb muscle veins in the short axis direction and calculating the volume change of the lower limb muscle veins in the short axis direction. The specific calculation process is as follows: Muscular veins can be approximated as multiple cylinders with elliptical cross-sections, spaced d centimeters apart, arranged along their major axis. The total volume V is the sum of the areas of each cross-section multiplied by the interval d, i.e. Where Ai is the area of ​​the i-th cross-section. The volume change ΔV is the difference between the volume after motion and the original volume, i.e. If we assume that the semi-major axis ai of all cross-sections is the same, i.e., ai = a, then Σ, where Σ ranges from i=1 to n. Therefore, the change in the minor axis of each cross-section is Si, resulting in a change in area. (Assuming the major axis remains constant). Assumption: 1. There are n motion acquisition points along the long axis of the lower limb muscle veins, spaced d centimeters apart, and each acquisition point yields an elliptical cross-section; 2. Assume that the major semi-axis of the elliptical cross-section at each sampling point i is ai and the minor semi-axis is bi when at rest. When in motion, the minor semi-axis becomes bi + Si (assuming Si is the deformation amplitude in the direction of the minor axis).

[0053] 3. The motion amplitude Si of each acquisition point i directly corresponds to the deformation of the minor axis of the elliptical cross section.

[0054] 4. The volume change ΔV is obtained by summing the changes in area of ​​each cross-section by the interval d, i.e. .

[0055] 5. Therefore, the area change of each cross-section can be approximated as: .

[0056] In summary, the amplitude of movement and volume change of lower limb muscle veins along the short axis can be modeled in the following ways: 1) Amplitude of movement of lower limb muscles and veins in the short axis direction (S1-S2) n Calculation of ) See Figure 9 As shown, Figure 9 This is a schematic diagram of the optimal sampling line (short axis view) for selecting veins using a single flexible array element according to the present invention. Figure 9 The red circular pattern represents the myoartery, and the blue oval pattern represents the myovenous vein. When the muscle contracts actively or after electrical stimulation, the myoartery does not deform, but the myovenous vein will deform. Therefore, the sampling line of this invention screens myovenous veins, and the myovenous vein with the largest short-axis mobility is selected by sector scanning.

[0057] Minor axis deformation S at each sampling point i i The change is determined by the minor axis of the elliptical cross section; let the minor axis be bi when at rest, and become bi+Si after motion; if only the change of the minor axis is considered, the formula for the motion amplitude Si is: (1); In formula (1), Δbi is the deformation of the short semi-axis of the i-th section.

[0058] 2) Calculation of the volume change (ΔV) of the lower limb muscles and veins along the short axis: See Figure 10 As shown, Figure 10 This is a schematic diagram of the spacing between multiple flexible array elements and the scanning plane (major axis view) of the present invention; Figure 10 In the image below, green indicates that the image or detection is normal, yellow indicates that an action needs to be taken, and red indicates that the warning conditions have been met.

[0059] Assume the muscular vein is divided into n segments along its major axis, with a distance of d centimeters between each segment, and the semi-major axis of the ellipse is ai; the volume change is the cumulative change in area of ​​each cross-section and the distance d, and the formula for the volume change ΔV is: (2); Further simplification of formula (2); under the condition that the semi-major axis a0 of the elliptical cross section remains constant and the distance d between each sampling point is fixed, the formula for the volume change ΔV can be simplified as follows: (3); In formula (3), a0 is the average semi-major axis (unit: cm) of all elliptical cross sections; S i denoted as the minor axis movement amplitude of the i-th sampling point, i.e., the change in the minor semi-axis; n is the total number of sampling points; and d is the distance between adjacent sampling points (unit: centimeters).

[0060] The method for calculating the amplitude of movement and volume change of lower limb muscle veins along the short axis in this invention is only applicable to the following conditions: 1. Uniformity of muscle and vein morphology: It is assumed that the muscle and vein morphology is uniform along the long axis, and the differences in the long semi-axis of each section can be ignored.

[0061] 2. Simplified movement pattern: The effects of muscle and vein torsion and non-uniform contraction are ignored, and only deformation in the short axis direction is considered.

[0062] 3. Stride length related parameters: The semi-major axis a0 can be estimated by referring to the average human stride length data (such as 65cm).

[0063] Based on the real-time matching results of muscle and vein deformation and electromyographic characteristics, the local data processing unit 4 can directly assess the impact of overall contraction / extension of the lower limb muscles and veins on volume by using the calculated amplitude of movement and volume change of the lower limb muscles and veins in the short axis direction; or, by using the calculated amplitude of movement and volume change of the lower limb muscles and veins in the short axis direction, combined with parameters including cadence and stride, a quantitative model of muscle pumping efficiency and exercise load can be established to further assess the lower limb muscle pumping efficiency.

[0064] Therefore, the formula advantages of the method for calculating the amplitude of movement and volume change of the lower limb muscle veins in the short axis direction according to the present invention are as follows: 1. High computational efficiency: Only the vertical displacement S at each point is measured. i There is no need to obtain the major semi-axis data separately.

[0065] 2. Clear physical meaning: It directly reflects the effect of the overall contraction / extension of muscles and veins on volume.

[0066] 3. Compatible with motion analysis: It can be combined with parameters such as cadence and stride length to evaluate muscle pump efficiency.

[0067] Preferred, see Figure 11 As shown, the dynamic matching calculation process is as follows: 1) Preoperative preparation: Resting state monitoring: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the baseline values ​​of the lower limb venous deformation volume V0 and the lower limb E0 EMG signals were measured at rest. Maximum contraction test: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the muscle deformation rate Dmax and Emax of the lower limb muscles during active contraction were recorded; Baseline database establishment: A baseline database is established using the measured baseline values ​​V0 of the lower limb venous deformation volume and E0 of the lower limb electromyography signal, as well as the recorded muscle deformation rate Dmax and electromyography signal Emax during active contraction of the lower limb muscles. Key parameter storage: Save the baseline value V0 of the measured lower limb venous deformation volume, i.e., the venous volume in the supine position; save the baseline value E0 of the measured lower limb electromyography signal, i.e., the resting electromyography amplitude; save the recorded muscle deformation rate Dmax during active lower limb muscle contraction, i.e., the maximum deformation rate; save the recorded electromyography signal Emax during active lower limb muscle contraction, i.e., the maximum contractile electromyography. 2) Postoperative real-time monitoring: Dual-modal data acquisition: The ultrasound array and the electromyography sensor array are used to measure the recorded value Vt of the venous deformation volume of the lower limbs of the postoperative patient and the recorded value Et of the electrical signal intensity of the muscles of the lower limbs of the postoperative patient, respectively. Dynamic matching calculation: The local data processing unit uses the recorded value Vt of the postoperative lower limb venous deformation volume and the recorded value E0 of the postoperative lower limb electromyography signal, along with the baseline database, to perform dynamic matching calculation. The calculation formula is as follows: Match degree M = (Vt / V0) × (Et / E0); Triggering condition: When the matching degree M > 0.5, the gradient strength decision phase is initiated.

[0068] Step 3, Gradient Strength Decision Stage: The data processing unit 4 selects the corresponding gradient electrical stimulation scheme based on the real-time matching results of muscle vein deformation-electromyographic characteristics.

[0069] The gradient electromagnetic scheme was developed in advance based on historical electromyography data. See also Figure 12 As shown, taking postoperative rehabilitation as an application scenario, the 'gradient stimulation protocol' uses preoperative lower limb venous volume changes monitored by flexible ultrasound to match electromyographic signals, and the postoperative electromyographic stimulation intensity ranging from 50% of the historical average to 80% of the historical maximum value to terminate. This gradient stimulation protocol includes at least an initial electrical stimulation intensity and three levels of gradient electrical stimulation intensity. The initial electrical stimulation intensity is set to 50% of the current intensity that enables the electromyographic amplitude of the lower limb muscles to reach the maximum value of the electromyographic signal Emax when the muscles actively contract. The current intensity that can increase the blood flow velocity in the lower limbs by 20% is set as the first level of electrical stimulation intensity, i.e., 50%-70% intensity. The current intensity that enables the lower limb electromyography amplitude to reach 80% of the electromyography signal Emax when the lower limb muscles actively contract is set as the second level of electrical stimulation intensity, i.e., 70%-85% intensity. The current intensity that can make the lower limb muscle deformation rate reach more than 80% of the muscle deformation rate Dmax when the lower limb muscles actively contract is set as the third level of electrical stimulation intensity, that is, 85%-100% intensity.

[0070] Step 4, Electrical Stimulation Phase: The local data processing unit 4 controls the electrical stimulation array through a programmable multi-channel current generator to perform electrophysiological stimulation intervention on the patient's lower limbs according to the selected gradient electrical stimulation scheme, with the corresponding current intensity.

[0071] Preferred, see Figure 12 As shown, the strategy of the gradient electrical stimulation scheme is as follows: After initializing the stimulation intensity, the lower limb muscles are first electrically stimulated using the initial electrical stimulation intensity. Then, the lower limbs are sequentially stimulated using a three-level gradient electrical stimulation intensity. During each level of gradient stimulation, the electrical stimulation intensity is determined to be up to standard by real-time monitoring of the ultrasound blood flow spectrum and electrode impedance changes. If it is not up to standard, the electrical stimulation intensity is reduced by 10% to optimize the contact of the electromyography sensing / electrical stimulation array module 3. If it is up to standard, the current electrical stimulation intensity is maintained for 3 minutes. Then, it is determined whether the current electrical stimulation intensity has reached the third level of electrical stimulation intensity. If it has not reached the third level, it is automatically upgraded to the next level of electrical stimulation. If it has reached the third level, the treatment is completed, and a lower limb electromyography-venous deformation matching map is generated. Example

[0072] This embodiment provides a method for fabricating the above-mentioned flexible ultrasound electrophysiological probe based on lower limb muscle vein deformation-electromyography matching, including the following steps: Step 1: Pretreatment of the thin flexible substrate: A thin, flexible substrate is made using polydimethylsiloxane (PDMS) or silicone composite material. First, several ultrasonic unit cavities are arranged in an array on one side surface of the thin, flexible substrate, and several electrode unit cavities are arranged in an array on the other side surface of the thin, flexible substrate. The number of ultrasonic unit cavities and electrode unit cavities are equal, and their positions do not overlap. Then, a micron-scale protrusion structure array is formed on the bottom surface of each electrode unit cavity using a molding process, and then activated with O2 plasma.

[0073] Preferably, the temperature of the silicon mold during compression molding is 80°C.

[0074] Preferably, the height of each protrusion in the micron-scale protrusion structure array is 5-20 μm, more preferably 15±3 μm, and the spacing between two adjacent protrusion structures is 30-100 μm.

[0075] Preferably, the activation time of O2 plasma is 30s.

[0076] Step 2, Construction of the ultrasound array: Using inkjet printing technology, a piezoelectric ceramic / polymer composite film capable of emitting 0.1-5MHz M-type broadband ultrasonic waves is deposited in the cavity of each ultrasonic unit. Each piezoelectric ceramic / polymer composite film serves as an ultrasonic unit, and all ultrasonic units form an ultrasonic array on one side surface of the thin flexible substrate.

[0077] Step 3: Construction of the electromyography / electrical stimulation array: First, CuNWs / PEDOT hybrid conductive ink is selected as the raw material. Aerosol jet deposition or inkjet printing technology is used to deposit or print an electromyography (EMG) sensing / electrostimulation electrode within each electrode cavity. During the deposition or printing process, an asymmetric micron-sized pore or mesh array is formed on the bottom surface of each EMG sensing / electrostimulation electrode. This asymmetric micron-sized pore or mesh array and the micron-sized protrusion array form a mechanical interlocking structure, thereby enhancing the adhesion of the EMG sensing / electrostimulation electrode within the electrode cavity. Each EMG sensing / electrostimulation electrode serves as an electrode unit, and all electrode units form an electrode array on the other side of the thin flexible substrate, i.e., an EMG sensing / electrostimulation array. Then, an antioxidant layer is coated on the surface of the electrode array, followed by photocuring and post-processing.

[0078] At this time, the ultrasonic array and the electrode array are arranged in a staggered manner on both sides of the thin flexible substrate. That is, the electrode unit of the electrode array is located in the gap between the ultrasonic units of the ultrasonic array. The electrode unit of the electrode array and the ultrasonic unit of the ultrasonic array are staggered and do not overlap. A pair of ultrasonic units and electrode units with close positions are paired to form an ultrasonic-electromyography matching unit.

[0079] Preferably, the mass ratio of CuNWs to PEDOT is (3-8):(2-5), more preferably 4:3; the diameter of CuNWs is 60-120nm, more preferably 80-100nm; the aspect ratio of CuNWs is >200; the coating rate of PEDOT on the surface of CuNWs is 70-90%; the nozzle diameter during deposition or printing is 50μm, and the deposition or printing speed is 8mm / s.

[0080] Preferably, the thickness of the electrode array is controlled between 5-15 μm, more preferably 12±2 μm, to achieve conformal fit with the skin surface; the surface roughness of the electrode array Ra=0.5-1.5 μm; the pore size of the plum blossom-shaped fractal hole structure is 0-180 μm, the porosity is 25-40%, and the pore wall width is 10-30 μm.

[0081] Preferably, the antioxidant layer is a SiO2 / TiO2 composite antioxidant layer with a thickness of 50-80 nm; the photocuring parameters are: UV wavelength of 365 nm and exposure intensity of 50 mW / cm². 2 The curing time is 90 seconds; the post-treatment operation is annealing at 80°C for 15 minutes.

[0082] Step 4: Construction of the flexible circuit board: First, the designed electromyography-ultrasound data fusion algorithm and gradient electrical stimulation scheme are stored in the local data processing unit with an embedded AI chip. Then, the local data processing unit and the programmable multi-channel current generator are installed on a flexible circuit board. Next, the flexible circuit board is attached to the surface of one end of the thin flexible substrate. Finally, a communication connection is established between the flexible circuit board and the constructed ultrasound array and electromyography sensing / electrical stimulation array via Bluetooth 5.2 / Wi-Fi, thereby realizing a flexible patch probe with a three-in-one function of signal acquisition, processing and stimulation output. Example

[0083] Based on the array-type flexible electrophysiological probe proposed in Example 1, and further based on the preparation method proposed in Example 2, see [link to Example 2]. Figure 13 As shown, this embodiment provides a distributed flexible electrophysiological probe. Specifically, the single sheet of flexible substrate used in Embodiment 1 is divided into several smaller sheets. Then, an equal number of ultrasound units and electrode units (the electromyography (EMG) sensing unit and the electrical stimulation unit share the same electrode unit) are arranged on both sides of each smaller sheet. The ultrasound units and electrode units on the same sheet are staggered, do not overlap, and are paired to form an ultrasound-EMG matching unit. The arrangement of the ultrasound-EMG matching unit can be linear or as shown in the diagram. Figure 13 The array shown Figure 13 The image shows an ultrasound-EMG matching module consisting of 2×4 ultrasound-EMG matching units mounted on a single small-sized, thin, flexible substrate (reduced number of units and increased spacing reduce interference between electrodes). During monitoring, multiple ultrasound-EMG matching modules can be distributed and applied to different parts of the patient's lower limb as needed. See [link to documentation]. Figure 14 As shown, the upper module is attached to the popliteal vein, the middle module is attached to the gastrocnemius vein, and the lower module is attached to the soleus vein. Example

[0084] Based on the structures of Examples 1 and 3, the flexible ultrasound electrophysiological probe of the present invention, based on lower limb muscle venous deformation-electromyography matching, can be further fabricated into a flexible wearable device integrating ultrasound imaging, electromyography monitoring, and electrophysiological stimulation functions. It is particularly suitable for dynamic assessment of lower limb muscle pumping function and for sports injury early warning and rehabilitation intervention scenarios. Using the flexible ultrasound electrophysiological probe of the present invention, the pumping function of lower limb muscles during exercise can be monitored and evaluated in real time. The flexible ultrasound electrophysiological probe of the present invention can provide a basis for sports injury early warning and, in rehabilitation intervention, adjust the electrostimulation protocol based on real-time monitoring data to promote the rehabilitation process.

[0085] The flexible ultrasound electrophysiology probe based on lower limb muscle vein deformation-electromyography matching of the present invention can also be applied in the following scenarios: 1. Sports Medicine: Monitoring the pumping efficiency of the quadriceps muscle during squats and sprints in athletes; 2. Preventive medicine: Early warning of subclinical high-risk groups, including those with lower extremity venous diseases such as deep vein thrombosis and varicose veins; 3. Rehabilitation therapy: Combine FES (functional electrical stimulation) technology to improve lower limb muscle atrophy in stroke patients.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A flexible electrophysiological probe based on muscle vein deformation-electromyography matching, characterized in that, include: The ultrasound module, which contains multiple ultrasound units, is responsible for acquiring real-time ultrasound data of the patient's lower limb muscles by emitting broadband ultrasound waves, thereby dynamically capturing the muscle contraction deformation and changes in the diameter of the veins in the patient's lower limbs. The electromyography (EMG) sensing module contains multiple EMG sensing units, which are responsible for collecting EMG data of the patient's lower limbs in real time, thereby simultaneously acquiring the EMG signals and local muscle fiber stress distribution on the surface of the patient's lower limbs. The number of ultrasound units is equal to the number of electromyography (EMG) sensing units. Each ultrasound unit is paired with its corresponding EMG sensing unit to form an ultrasound-EMG matching unit. The local data processing unit is responsible for performing electromyography-ultrasound data fusion analysis on the collected muscle ultrasound data and electromyography induction data to obtain the real-time matching results of muscle vein deformation-electromyography features, the evaluation results of the impact of overall contraction / extension of lower limb muscle veins on volume, and the evaluation results of lower limb muscle infusion efficiency. Based on the obtained real-time matching results of muscle vein deformation-electromyography features, the unit makes gradient electrical stimulation scheme decisions. The electrical stimulation module contains multiple electrical stimulation units, which are responsible for outputting stimulation currents of corresponding intensities according to the determined gradient electrical stimulation scheme, so as to realize electrophysiological stimulation intervention on the patient's lower limbs. The number of the electrical stimulation units is equal to the number of the electromyographic sensing units; A thin, flexible substrate is used to integrate the ultrasound array, the electromyography sensing array, the electrical stimulation array, and the local data processing unit into a flexible, heterogeneous integrated device, thereby enabling it to conformally adhere to the surface of the patient's lower limb.

2. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 1, characterized in that, The local data processing unit achieves electromyography-ultrasound data fusion analysis through a built-in embedded AI chip, specifically using the following method: First, the obtained muscle ultrasound data and electromyography data are synchronized using a data synchronization engine; Then, muscle vein volume feature extraction and electromyography feature extraction are performed on the synchronized muscle ultrasound data and electromyography data, respectively. Next, the extracted muscle vein volume features and electromyographic features are sent to the spatiotemporal alignment matrix for spatiotemporal data synchronization and calibration; Then, the muscle-venous volume characteristics and electromyographic characteristics, after being synchronized and calibrated with spatiotemporal data, are sent to the multimodal electromyographic-ultrasound matching model; Finally, the multimodal electromyography-ultrasound matching model performs dynamic matching calculations and outputs real-time matching results of muscle vein deformation and electromyography features.

3. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 2, characterized in that, The extraction of the muscle vein volume characteristics includes calculating the amplitude of movement of the lower limb muscle veins in the short axis direction and calculating the volume change of the lower limb muscle veins in the short axis direction. The specific calculation process is as follows: 1) Calculate the amplitude of movement of the lower limb muscles and veins along the short axis using the following formula: （1）； In formula (1), S i The minor axis deformation for each acquisition point i; Δbᵢ is the deformation of the short semi-axis of the i-th section; 2) Calculate the volume change of the lower limb muscles and veins along the short axis using the following formula: （3）； In formula (3), a0 is the average semi-major axis of all elliptical cross sections, in centimeters; S i The minor axis movement amplitude of the i-th acquisition point, i.e., the change in the minor semi-axis; n is the total number of data collection points; d represents the distance between adjacent sampling points, in centimeters.

4. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 3, characterized in that, The local data processing unit can directly assess the impact of overall contraction / extension of the lower limb muscles and veins on volume by using the calculated amplitude of movement and volume change of the lower limb muscles and veins in the short axis direction. And / or, the local data processing unit can use the calculated amplitude of movement of the lower limb muscle veins in the short axis direction and the volume change of the lower limb muscle veins in the short axis direction, and combine them with parameters including cadence and stride length, to establish a quantitative model of muscle pumping efficiency and exercise load, and evaluate the lower limb muscle pumping efficiency.

5. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 2, characterized in that, The dynamic matching calculation process is as follows: 1) Preoperative preparation: Resting state monitoring: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the baseline values ​​of the lower limb venous deformation volume V0 and the lower limb E0 EMG signals were measured at rest. Maximum contraction test: Using the hospital's existing ultrasound equipment and electromyography (EMG) sensing equipment, the muscle deformation rate Dmax and Emax of the lower limb muscles during active contraction were recorded; Baseline database establishment: A baseline database is established using the measured baseline values ​​V0 of the lower limb venous deformation volume and E0 of the lower limb electromyography signal, as well as the recorded muscle deformation rate Dmax and electromyography signal Emax during active contraction of the lower limb muscles. Key parameter storage: Save the baseline value V0 of the measured lower limb venous deformation volume, i.e., the venous volume in the supine position; save the baseline value E0 of the measured lower limb electromyography signal, i.e., the resting electromyography amplitude; save the recorded muscle deformation rate Dmax during active lower limb muscle contraction, i.e., the maximum deformation rate; save the recorded electromyography signal Emax during active lower limb muscle contraction, i.e., the maximum contractile electromyography. 2) Postoperative real-time monitoring: Dual-modal data acquisition: The ultrasound module and the electromyography sensing module respectively measure the recorded value Vt of the venous deformation volume of the lower limb of the patient after surgery and the recorded value Et of the electrical signal intensity of the muscle of the lower limb of the patient after surgery. Dynamic matching calculation: The local data processing unit uses the recorded value Vt of the postoperative lower limb venous deformation volume and the recorded value E0 of the postoperative lower limb electromyography signal, along with the baseline database, to perform dynamic matching calculation. The calculation formula is as follows: Match degree M = (Vt / V0) × (Et / E0); Triggering condition: When the matching degree M > 0.5, the gradient strength decision stage is initiated.

6. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 1, characterized in that, The gradient electrical stimulation protocol includes at least an initial electrical stimulation intensity and three levels of gradient electrical stimulation intensity, wherein, The initial electrical stimulation intensity is set to 50% of the current intensity that enables the electromyographic amplitude of the lower limb muscles to reach the maximum value of the electromyographic signal Emax when the muscles actively contract. The current intensity that can increase the blood flow velocity in the lower limbs by 20% is set as the first level of electrical stimulation intensity, i.e., 50%-70% intensity. The current intensity that enables the lower limb electromyography amplitude to reach 80% of the electromyography signal Emax when the lower limb muscles actively contract is set as the second level of electrical stimulation intensity, i.e., 70%-85% intensity. The current intensity that can make the lower limb muscle deformation rate reach more than 80% of the muscle deformation rate Dmax when the lower limb muscles actively contract is set as the third level of electrical stimulation intensity, that is, 85%-100% intensity.

7. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 1, characterized in that, The current intensity of each of the electrical stimulation units is adjusted by a programmable multi-channel current generator integrated on the thin flexible substrate. The programmable multi-channel current generator supports bidirectional 0.1-100mA pulse width modulation and dynamically adjusts the electrical stimulation intensity according to the impedance matching algorithm to achieve precise control and adaptive adjustment of the electrical stimulation parameters.

8. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 1, characterized in that, The electromyography sensing unit and the electrical stimulation unit are the same electrode unit that has both electromyography sensing and electrical stimulation functions. The receiving of electrical signals corresponds to electromyography signal induction, and the release of electrical signals corresponds to electrical stimulation triggering. Each ultrasound unit and its corresponding electrode unit are paired to form an ultrasound-electromyography matching unit.

9. The flexible electrophysiological probe based on muscle vein deformation-electromyography matching according to claim 1, characterized in that, The ultrasound-electromyography matching unit is applied to the surface of the patient's lower limb muscles using an array or distributed structure. The array-like distribution structure is as follows: all the ultrasound-electromyography matching units are concentrated on the same large-size thin flexible material. Specifically, all the ultrasound units are arranged in an array on one side surface of the large-size thin flexible material to form an ultrasound array, and all the electromyography sensing units are arranged in an array on the other side surface of the large-size thin flexible material to form an electromyography sensing array. Furthermore, the ultrasound array and the electromyography sensing array are staggered on both sides of the large-size thin flexible substrate. That is, the array elements of the electromyography sensing array are located in the gaps between the array elements of the ultrasound array. The array elements of the electromyography sensing array and the array elements of the ultrasound array are staggered and do not overlap. A pair of ultrasound units and electromyography sensing units that are close in position are paired to form an ultrasound-electromyography matching unit. The distributed structure is as follows: all the ultrasound-electromyography matching units are dispersed in groups of one unit on several small-sized thin flexible materials. Specifically, several ultrasound units are evenly arranged on one side surface of each small-sized thin flexible material, and several electromyography sensing units are evenly arranged on the other side surface of each small-sized thin flexible material. The number of ultrasound units and electromyography sensing units on the same small-sized thin flexible material is equal. Furthermore, the ultrasound units and electromyography sensing units located on the same small-sized thin flexible material are staggered and do not overlap. A pair of ultrasound units and electromyography sensing units located close to each other are paired to form an ultrasound-electromyography matching unit.

10. A method for fabricating a flexible electrophysiological probe based on muscle-venous deformation-electromyography matching as described in any one of claims 1-9, characterized in that, Includes the following steps: Step 1: Pretreatment of the thin flexible substrate: A thin flexible substrate is made of polydimethylsiloxane or silicone composite material. First, several ultrasonic unit cavities are arranged in an array on one side surface of the thin flexible substrate, and several electrode unit cavities are arranged in an array on the other side surface of the thin flexible substrate. The number of ultrasonic unit cavities and electrode unit cavities are equal and their positions do not overlap. Then, a micron-scale protrusion structure array is formed on the bottom surface of each electrode unit cavity using a molding process, and then activated with O2 plasma. Step 2, Construction of the ultrasound array: Using inkjet printing technology, a piezoelectric ceramic / polymer composite film capable of emitting 0.1-5MHz M-mode broadband ultrasonic waves is deposited in the cavity of each ultrasonic unit. Each piezoelectric ceramic / polymer composite film serves as an ultrasonic unit, and all ultrasonic units form an ultrasonic array on one side surface of the thin flexible substrate. Step 3: Construction of the electromyography / electrical stimulation array: First, CuNWs / PEDOT hybrid conductive ink is selected as the raw material. Aerosol jet deposition or inkjet printing technology is used to deposit or print an electromyography (EMG) sensing / electrostimulation electrode within each electrode cavity. During the deposition or printing process, an asymmetric micron-sized pore or mesh array is formed on the bottom surface of each EMG sensing / electrostimulation electrode. This asymmetric micron-sized pore or mesh array and the micron-sized protrusion array form a mechanical interlocking structure, thereby enhancing the adhesion of the EMG sensing / electrostimulation electrode within the electrode cavity. Each EMG sensing / electrostimulation electrode serves as an electrode unit, and all electrode units form an electrode array on the other side of the thin flexible substrate, i.e., an EMG sensing / electrostimulation array. Then, an antioxidant layer is coated on the surface of the electrode array, followed by photocuring and post-processing. At this time, the ultrasonic array and the electrode array are arranged in a staggered manner on both sides of the thin flexible substrate. That is, the electrode unit of the electrode array is located in the gap between the ultrasonic units of the ultrasonic array. The electrode unit of the electrode array and the ultrasonic unit of the ultrasonic array are staggered and do not overlap. A pair of ultrasonic units and electrode units with close positions are paired to form an ultrasonic-electromyography matching unit. Step 4: Construction of the flexible circuit board: First, the designed electromyography-ultrasound data fusion algorithm and gradient electrical stimulation scheme are stored in the local data processing unit with an embedded AI chip. Then, the local data processing unit and the programmable multi-channel current generator are installed on a flexible circuit board. Next, the flexible circuit board is attached to the surface of one end of the thin flexible substrate. Finally, a communication connection is established between the flexible circuit board and the constructed ultrasound array and electromyography sensing / electrical stimulation array via Bluetooth 5.2 / Wi-Fi, thereby realizing a flexible patch probe with a three-in-one function of signal acquisition, processing and stimulation output.

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