Portable high-density myoelectricity acquisition equipment

Abstract

The portable high-density myoelectricity acquisition equipment provided by the invention has three core advantages of high precision, strong adaptability and clinical practicability, the optimal channel is dynamically activated through the topological self-adaptive electrode array, the skin curvature adjustment is detected in real time through the piezoresistive sensor, the motion artifact interference is remarkably reduced, and the channel utilization rate is remarkably improved; the Cobb angle prediction equation realizes that the dynamic prediction error of the Cobb angle is smaller, and the precision bottleneck in the prior art is broken through; and furthermore, through a closed-loop feedback system, the daily action correction compliance of the patient is obviously improved. According to the device, AIS full-pathological cycle paravertebral muscle function quantitative evaluation is completed on the portable device for the first time, and a wearable precise intervention tool is provided for clinic.

Description

Technical Field

[0001] The present invention relates to the research field of adolescent AIS, and in particular to a portable high-density electromyography acquisition device. Background Art

[0002] Adolescent idiopathic scoliosis (AIS) is the most common three-dimensional spinal deformity, and its pathological progression is significantly correlated with paraspinal muscle dysfunction. In recent years, quantitative muscle assessment based on surface electromyography (sEMG) has become a research hotspot in AIS, particularly high-density electromyography (HD-sEMG), which uses a two-dimensional electrode array to capture the spatiotemporal characteristics of muscle activity.

[0003] Existing technologies, such as patent document CN 118656673A, use a 64-channel array combined with a convolutional blind source separation algorithm to decompose motion units. Patent document CN 118000744A proposes a distributed 8×64-channel acquisition system to support multi-muscle group monitoring. Patent document CN 118021326A further integrates wireless synchronous acquisition with motion capture. These solutions provide the hardware foundation for muscle function analysis.

[0004] However, existing technologies for monitoring the entire pathological cycle of AIS still have serious limitations:

[0005] Channel redundancy and motion artifacts: The fixed 64-channel design generates a large amount of invalid data in dynamic postures and cannot adapt to skin deformation caused by spinal curvature, introducing motion artifacts.

[0006] Lack of pathology-specific models: Existing EMG decoding algorithms focus on general motor unit decomposition and have not established a quantitative correlation between paraspinal muscle asymmetry and Cobb angle evolution;

[0007] Lack of clinical practicality: Traditional devices rely on offline processing and cannot guide corrective movements in real time.

[0008] The above defects make it difficult for existing systems to meet the long-term dynamic monitoring needs of AIS, hindering the transformation of muscle quantitative assessment indicators into clinical practice.

[0009] Therefore, there is an urgent need to develop a portable high-density electromyography acquisition device to solve the above problems. Summary of the Invention

[0010] The purpose of the present invention is to provide a portable high-density electromyography acquisition device to solve the problems existing in the above-mentioned prior art.

[0011] To achieve the above object, the present invention provides the following solutions:

[0012] The present invention provides a portable high-density electromyography acquisition device, comprising:

[0013] A data acquisition unit, which is equipped with 64 acquisition channels for collecting electromyographic signals;

[0014] A data processing unit, which establishes communication with the data acquisition unit and is used to control the on and off of the acquisition channel, process the acquired data, and provide feedback on the acquired data;

[0015] A data receiving unit, which communicates wirelessly with the data processing unit and is used to receive and display processed data.

[0016] Preferably, the data acquisition unit includes a base film, on which 64 electrodes are distributed in an array, the electrodes establish communication with the data processing unit via data lines, and a conductive gel layer is provided on the surface of the electrodes.

[0017] Preferably, a piezoresistive sensor is provided between the base film and the electrode.

[0018] Preferably, the data processing unit comprises a housing, an integrated circuit board and a power supply are arranged in the housing, and a display screen is arranged on the surface of the housing.

[0019] Preferably, the integrated circuit board integrates an MCU and a topology adaptive circuit connected to the MCU, an integrated preamplifier, an ADC acquisition module, an FPGA signal processor, and a wireless communication module. The topology adaptive circuit is connected to the data acquisition unit, and the wireless communication module is wirelessly connected to the data receiving unit.

[0020] Preferably, the data receiving unit includes a server host and an AR visualization device, and the server host is wirelessly connected to a cloud server.

[0021] The present invention also provides a method for using a portable high-density electromyography acquisition device, comprising the following steps:

[0022] S1. Glue the data acquisition unit to the adolescent's spine.

[0023] S2. Power on the device;

[0024] S3. The piezoresistive sensor detects skin curvature and uploads the collected data;

[0025] S4. The data processing unit uses a built-in dynamic channel optimization algorithm to determine the optimal channel acquisition plan and dynamically adjusts the electrode activation area using a topology adaptive circuit.

[0026] S5. The electrodes collect electromyographic signals and upload the collected data;

[0027] S6. The data processing unit processes the electromyographic signal and calculates the muscle imbalance index, and generates a personalized training plan;

[0028] S7. The data receiving unit stores the data, and the AR visualization device generates a paraspinal muscle activation heat map and displays a personalized training plan.

[0029] Preferably, in step S4, the data processing unit obtains the optimal channel acquisition solution through the built-in dynamic channel optimization algorithm, including:

[0030] S41. Calculate the Spearman rank correlation coefficient between the 64 channels and the Cobb angle;

[0031] S42. Constructing a channel correlation matrix;

[0032] S43. Use the greedy algorithm to output the optimal subset of channels.

[0033] Preferably, the dynamic channel optimization algorithm is:

[0034]

[0035] Among them, C i ; θ is the mutual information between channel i and Cobb angle θ, ρ ij is the correlation coefficient of channel i, j, and λ = 0.7 is the redundancy penalty factor.

[0036] Preferably, the Cobb angle prediction equation is:

[0037]

[0038] Among them, α = 0.32, is the myoelectric weight, w i is the anatomical weight, κ t is the real-time spinal curvature, and ∈ is the individual correction factor.

[0039] Compared with the prior art, the present invention has achieved the following beneficial technical effects:

[0040] The portable, high-density electromyography acquisition device provided by this invention boasts three core advantages: high precision, strong adaptability, and clinical practicality. It dynamically activates the optimal channel through a topologically adaptive electrode array, and uses a piezoresistive sensor to detect skin curvature adjustments in real time, significantly reducing motion artifact interference and significantly improving channel utilization. The Cobb angle prediction equation achieves a smaller dynamic prediction error for the Cobb angle, breaking through the accuracy bottleneck of existing technologies. Furthermore, through a closed-loop feedback system, patients' compliance with daily movement corrections is significantly improved. This device, for the first time, enables the quantitative assessment of paraspinal muscle function throughout the entire AIS pathological cycle on a portable device, providing a wearable, precision intervention tool for clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 Schematic diagram of the portable high-density electromyography acquisition device provided by the present invention. DETAILED DESCRIPTION

[0043] 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 creative efforts are within the scope of protection of the present invention.

[0044] The purpose of the present invention is to provide a portable high-density electromyography acquisition device to solve the problems existing in the prior art.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1:

[0047] This embodiment provides a portable high-density electromyography acquisition device, such as Figure 1 As shown, including:

[0048] Data acquisition unit 1, which is equipped with 64 acquisition channels for collecting electromyographic signals;

[0049] Data processing unit 2, which establishes communication with data acquisition unit 1, is used to control the on and off of the acquisition channel, process the acquired data, and provide feedback on the acquired data;

[0050] The data receiving unit 3 communicates wirelessly with the data processing unit 2 and is used to receive, process and display data.

[0051] As an embodiment, the data acquisition unit 1 includes a base film, which can be a medical-grade polyimide film with a thickness of 0.1 mm and a Young's modulus of 2.5 GPa. 64 electrodes are distributed in an array on the base film, using an 8*8 array. The electrodes are laser-etched Ag / AgCl electrodes with a diameter of 5 mm and a spacing of 20 mm. The electrodes communicate with the data processing unit 2 through a data line. The surface of the electrode is provided with a water-based polymer conductive gel layer with an impedance of <10 kΩ.

[0052] As an embodiment, a piezoresistive sensor is provided between the base film and the electrode, and the piezoresistive sensor is used to detect the skin curvature and thus dynamically optimize the electrode activation topology.

[0053] As an embodiment, the data processing unit 2 includes a shell, which can adopt a medical-grade TPU shell and is processed by injection molding or 3D printing. An integrated circuit board and a power supply are provided inside the shell. The power supply adopts a rechargeable lithium polymer battery, 3.7V / 2000mAh. A display screen is provided on the surface of the shell, which can adopt an LCD touch screen to facilitate display and operation.

[0054] As an implementation method, the integrated circuit board integrates an MCU and a topology adaptive circuit connected to the MCU, an integrated preamplifier, an ADC acquisition module, an FPGA signal processor, and a wireless communication module. The MCU serves as the master control element, and the topology adaptive circuit is controlled by the TI ADS1299 chip. The input noise is 1.2μVpp, and the CMRR>110dB. The topology adaptive circuit is connected to the data acquisition unit. The sampling rate of the ADC acquisition module is 2kSPS / channel, and the dynamic range is 110dB. The FPGA signal processor uses the Xilinx Artix-7 series signal processor to realize real-time filtering and electromyography feature extraction. The wireless communication module can use a Bluetooth 5.2 or WiFi6 module and is wirelessly connected to the data receiving unit.

[0055] As an implementation method, the data receiving unit 3 includes a server host and an AR visualization device, and the server host is wirelessly connected to the cloud server.

[0056] Example 2:

[0057] This embodiment provides a method for using a portable high-density myoelectric acquisition device, comprising the following steps:

[0058] S1. Glue the data acquisition unit to the adolescent's spine.

[0059] S2. Power on the device;

[0060] S3. The piezoresistive sensor detects skin curvature and uploads the collected data;

[0061] S4. The data processing unit uses a built-in dynamic channel optimization algorithm to determine an optimal channel acquisition plan and dynamically adjusts the electrode activation area using a topology adaptive circuit. The optimal channel acquisition plan determined by the data processing unit using the built-in dynamic channel optimization algorithm includes:

[0062] S41. Calculate the Spearman rank correlation coefficient between the 64 channels and the Cobb angle;

[0063] S42. Constructing a channel correlation matrix;

[0064] S43. Use greedy algorithm to output the optimal subset of channels;

[0065] The dynamic channel optimization algorithm is:

[0066]

[0067] Among them, C i ; θ is the mutual information between channel i and Cobb angle θ, ρ ij is the correlation coefficient of channel i, j, λ = 0.7, is the redundancy penalty factor;

[0068] The Cobb angle prediction equation is:

[0069]

[0070] Among them, α = 0.32, is the myoelectric weight, w i is the anatomical weight, κ t is the real-time spinal curvature, ∈ is the individual correction factor;

[0071] S5. The electrodes collect electromyographic signals and upload the collected data;

[0072] S6. The data processing unit processes the EMG signals and calculates the muscle imbalance index. This can be calculated using the AIS prediction model, which is a multimodal fusion spatiotemporal graph convolutional network. By inputting EMG features, 3D spinal posture, and patient metadata, the model can predict AIS risk and generate a personalized training plan.

[0073] S7. The data receiving unit stores the data, and the AR visualization device generates a paraspinal muscle activation heat map and displays a personalized training plan.

[0074] The portable, high-density electromyography acquisition device provided by this invention boasts three core advantages: high precision, strong adaptability, and clinical practicality. It dynamically activates the optimal channel through a topologically adaptive electrode array, and uses a piezoresistive sensor to detect skin curvature adjustments in real time, significantly reducing motion artifact interference and significantly improving channel utilization. The Cobb angle prediction equation achieves a smaller dynamic prediction error for the Cobb angle, breaking through the accuracy bottleneck of existing technologies. Furthermore, through a closed-loop feedback system, patients' compliance with daily movement corrections is significantly improved. This device, for the first time, enables the quantitative assessment of paraspinal muscle function throughout the entire AIS pathological cycle on a portable device, providing a wearable, precision intervention tool for clinical practice.

[0075] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] It should be noted that the components mentioned in the above embodiments are all universal standard parts or components known to those skilled in the art, and their structures and principles can be known to those skilled in the art through technical manuals or conventional experimental methods.

[0077] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A portable high-density electromyography acquisition device, characterized by: include: A data acquisition unit, which is equipped with 64 acquisition channels for collecting electromyographic signals; A data processing unit, which establishes communication with the data acquisition unit and is used to control the on and off of the acquisition channel, process the acquired data, and provide feedback on the acquired data; A data receiving unit, which communicates wirelessly with the data processing unit and is used to receive and display processed data.

2. The portable high-density electromyography acquisition device according to claim 1, characterized in that: The data acquisition unit includes a base film, on which 64 electrodes are distributed in an array. The electrodes communicate with the data processing unit via data lines, and a conductive gel layer is provided on the surface of the electrodes.

3. The portable high-density electromyography acquisition device according to claim 2, characterized in that: A piezoresistive sensor is provided between the base film and the electrode.

4. The portable high-density myoelectric acquisition device according to claim 1, characterized in that: The data processing unit comprises a shell, an integrated circuit board and a power supply are arranged in the shell, and a display screen is arranged on the surface of the shell.

5. The portable high-density electromyography acquisition device according to claim 4, characterized in that: The integrated circuit board integrates an MCU and a topology adaptive circuit connected to the MCU, an integrated preamplifier, an ADC acquisition module, an FPGA signal processor, and a wireless communication module. The topology adaptive circuit is connected to the data acquisition unit, and the wireless communication module is wirelessly connected to the data receiving unit.

6. The portable high-density electromyography acquisition device according to claim 1, characterized in that: The data receiving unit includes a server host and an AR visualization device, and the server host is wirelessly connected to a cloud server.

7. The method for using the portable high-density myoelectric acquisition device according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Glue the data acquisition unit to the adolescent's spine. S2. Power on the device; S3. The piezoresistive sensor detects skin curvature and uploads the collected data; S4. The data processing unit uses a built-in dynamic channel optimization algorithm to determine the optimal channel acquisition plan and dynamically adjusts the electrode activation area using a topology adaptive circuit. S5. The electrodes collect electromyographic signals and upload the collected data; S6. The data processing unit processes the electromyographic signal and calculates the muscle imbalance index, and generates a personalized training plan; S7. The data receiving unit stores the data, and the AR visualization device generates a paraspinal muscle activation heat map and displays a personalized training plan.

8. The method for using the portable high-density myoelectric acquisition device according to claim 7, characterized in that: In step S4, the data processing unit uses a built-in dynamic channel optimization algorithm to obtain the optimal channel acquisition solution, including: S41. Calculate the Spearman rank correlation coefficient between the 64 channels and the Cobb angle; S42. Constructing a channel correlation matrix; S43. Use the greedy algorithm to output the optimal subset of channels.

9. The method for using the portable high-density electromyography acquisition device according to claim 8, characterized in that: The dynamic channel optimization algorithm is: Among them, C i ; θ is the mutual information between channel i and Cobb angle θ, ρ ij is the correlation coefficient of channel i, j, and λ = 0.7 is the redundancy penalty factor.

10. The method for using the portable high-density myoelectric acquisition device according to claim 9, characterized in that: The Cobb angle prediction equation is: Among them, α = 0.32, is the myoelectric weight, w i is the anatomical weight, κ t is the real-time spinal curvature, and ∈ is the individual correction factor.

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