Method and system for improving electromyography collection accuracy

Abstract

The present disclosure provides a method and system for improving the accuracy of electromyography collection, which comprises: collecting electromyography signals through a multi-channel acquisition module; filtering the collected electromyography signals, with an independent filter unit set for each acquisition channel; and performing noise calibration on the filtered data and RMS calculation on the noise-calibrated data. The method and system for improving the accuracy of electromyography collection provided by the present disclosure effectively improve the collection accuracy by setting an independent filter for each acquisition channel, remove the influence of the noise of the device itself on the accuracy of electromyography collection by performing noise calibration on the filtered signals, remove the influence of external environmental factors such as static electricity by setting a grounding module, and make the application of the collection method more flexible while improving the calculation efficiency by simplifying the calibration and calculation processes and reducing the requirement for the computing power of the operation-related modules.

Description

Technical Field

[0001] This disclosure relates to the field of electromyography (EMG) signal acquisition technology, and in particular to methods and systems for improving the accuracy of EMG acquisition. Background Technology

[0002] Surface electromyography (sEMG) is a weak physiological electrical signal generated by the human body, with an amplitude in the microvolt range, making it susceptible to external interference. Surface EMG acquisition is generally based on a simple conduction process: whenever a muscle contracts, electrical activity is generated, which is conducted through adjacent tissues and bones and then recorded by electrode pads on adjacent skin areas.

[0003] Because surface electromyography (EMG) signals are very weak, they are easily mixed with various noises, including noise from the EMG signal itself, motion artifacts, and environmental electromagnetic interference, during actual acquisition. Existing EMG acquisition methods have poor signal quality and require complex correction and calculation processes during signal processing, which reduces the overall efficiency of the system and places high demands on the system's computing power. Summary of the Invention

[0004] This disclosure addresses the aforementioned issues by proposing a method and system for improving the accuracy of electromyography (EMG) acquisition.

[0005] To solve at least one of the above-mentioned technical problems, this disclosure proposes the following technical solution:

[0006] Firstly, a method for improving the accuracy of electromyography (EMG) acquisition is provided, including the following steps:

[0007] The acquisition module acquires electromyographic signals through multiple channels;

[0008] The acquired electromyographic signals are filtered, with an independent filter unit set for each acquisition channel;

[0009] Noise calibration is performed on the filtered data.

[0010] Perform RMS calculations on the noise-calibrated data.

[0011] In some implementations, the acquisition frequency of the multi-channel electromyography signal is 205 Hz.

[0012] In some implementations, noise calibration of the filtered data includes:

[0013] Short-circuit the acquisition module.

[0014] Test the equipment noise according to the preset frequency value and record the noise value when short-circuited;

[0015] Use a signal generator to input a sine wave signal, test the device noise according to the preset RMS value, and record the noise value when the sine wave signal is input.

[0016] Based on the noise values ​​during short circuit and the noise values ​​when inputting a sine wave signal, the filtered data is processed to remove the device noise values ​​from the filtered data.

[0017] In some implementations, when the acquisition module is short-circuited, the preset frequency value is 205Hz.

[0018] In some implementations, the preset RMS value is 100 when a sine wave signal is input.

[0019] In some implementations, performing RMS calculations on the noise-calibrated data includes:

[0020] When the number of raw values ​​is M, the M raw values ​​are treated as an array, and an RMS value is calculated using the RMS calculation formula.

[0021] When the number of raw values ​​is M+1, remove the first raw value, and treat the second to the M+1th raw values ​​as an array. Calculate the next RMS value using the RMS calculation formula.

[0022] Secondly, a system for improving the accuracy of electromyography (EMG) acquisition is provided, for performing any of the above-mentioned methods for improving the accuracy of EMG acquisition, the system comprising:

[0023] The acquisition module is used for multi-channel acquisition of electromyographic signals;

[0024] The filtering module is used to filter the acquired electromyographic signals. The filtering module includes several independent filter units, and each acquisition channel in the acquisition module is equipped with an independent filter unit.

[0025] The noise calibration module is used to calibrate the noise in the filtered data.

[0026] The RMS calculation module is used to perform RMS calculations on the noise-calibrated data.

[0027] In some implementations, a grounding module is also included, which is used to ground the acquisition module.

[0028] In some implementations, a storage module is also included, which stores the acquired electromyographic signals, filtered data, noise-calibrated data, and the results calculated by RMS.

[0029] The beneficial effects of this disclosure are: by setting an independent filter for each acquisition channel, the acquisition accuracy is effectively improved; by performing noise calibration on the filtered signal, the influence of the device's own noise on the electromyography acquisition accuracy is removed; by setting a grounding module, the influence of external environmental factors such as static electricity is removed; the calibration and calculation processes are simple, and the computing power requirements of the calculation-related modules are low, which improves the calculation efficiency and makes the application of the acquisition method more flexible.

[0030] In addition, unless otherwise specified, all technical solutions disclosed herein can be implemented using conventional methods in the field. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating a method for improving the accuracy of electromyography (EMG) acquisition, provided as an embodiment of this disclosure.

[0033] Figure 2 This is a schematic diagram of a system for improving the accuracy of electromyography (EMG) acquisition, provided as an embodiment of the present disclosure. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only some, not all, of the embodiments of this disclosure, and are used merely to explain this disclosure and are not intended to limit it. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0035] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0036] Example 1:

[0037] Reference manual attached Figure 1This paper illustrates a method for improving the accuracy of electromyography (EMG) acquisition according to an embodiment of this application, which specifically includes the following steps:

[0038] S1: The acquisition module acquires electromyographic signals through multiple channels;

[0039] S2: The acquired electromyography signals are filtered, and an independent filter unit is set for each acquisition channel;

[0040] S3: Perform noise calibration on the filtered data.

[0041] S4: Perform RMS calculation on the noise-calibrated data.

[0042] In an optional embodiment, before the electromyography (EMG) signal acquisition begins in S1, the acquisition module is grounded to eliminate the influence of external uncertainties such as static electricity on the accuracy of EMG acquisition.

[0043] In an optional embodiment, in S1, the acquisition frequency of the multi-channel electromyography (EMG) signal acquisition can be selected as 205Hz, thereby effectively preventing the alternating current from affecting the acquired EMG signals. Specifically, the acquisition module can be connected to electrodes, which are fixed at the location of the muscle to be tested on the human body, and the acquisition module can acquire EMG signals in real time.

[0044] In an optional embodiment, in S2, each acquisition channel is provided with an independent filter unit, and the filtering algorithms in each independent filter unit can be different. This avoids the buffering time and numerical fluctuations that occur when multiple channels share a single filter due to the high filter reception speed. Independent filters effectively improve acquisition accuracy, eliminate the limitation on filter reception speed when sharing a single filter, and improve acquisition efficiency.

[0045] Specifically, the filtered data can include raw values, noise values, and thresholds.

[0046] In an optional embodiment, in S3, noise calibration of the filtered data includes:

[0047] Short-circuit the acquisition module. When the acquisition module is connected to the electrode, short-circuit the connection line between the electrode and the acquisition module.

[0048] Test the equipment noise according to the preset frequency value, and record the noise value when short-circuited after filtering. The preset frequency value can be 205Hz, which can remove the influence of 50Hz and its multiples in the power frequency AC.

[0049] Use a signal generator to input a sine wave signal, test the device noise according to the preset RMS value, and record the noise value when the sine wave signal is input after filtering. At this time, the preset RMS value can be 100.

[0050] Based on the noise values ​​during short circuit and the noise values ​​when inputting a sine wave signal, the filtered data is processed to remove the device noise values ​​from the filtered data.

[0051] The "equipment" refers to the device for acquiring electromyographic (EMG) signals. Generally, an EMG signal acquisition device can include an acquisition module and a filtering module. The acquisition module is connected to the electrodes and is used to execute S1. The input of the filtering module is connected to the output of the acquisition module, and the filtering module is used to execute S2. Since the device also generates noise during operation, affecting the accuracy of EMG signal acquisition, it is necessary to calibrate the noise generated by the device itself to remove its influence on EMG signal acquisition and improve the accuracy of EMG signal acquisition.

[0052] In S3, the noise values ​​of each device when short-circuited and when a sine wave signal is input can be measured in advance and the measured data can be stored. When noise calibration of the current data is required, the data points of the two noise values ​​corresponding to the device currently acquiring the electromyography signal can be read from the storage module. The noise value change of the acquisition device itself can be regarded as a linear change. The RMS value is calculated based on the filtered data, thereby obtaining the noise value of the current acquisition device. The noise value of the device itself is removed from the filtered data, thus completing the noise calibration.

[0053] In an optional embodiment, in S4, performing an RMS calculation on the noise-calibrated data includes:

[0054] Obtain the raw value from the noise-calibrated data;

[0055] When the number of raw values ​​accumulates to M, the M raw values ​​are treated as an array, and an RMS value is calculated using the RMS calculation formula.

[0056] When the number of raw values ​​is M+1, remove the first raw value, and treat the second to the M+1th raw values ​​as an array. Calculate the next RMS value using the RMS calculation formula.

[0057] This process continues until the last raw value is included in the RMS calculation and an RMS value is obtained.

[0058] Calculate the average of all RMS values ​​obtained in the above steps.

[0059] Therefore, by making the data in the array change in a rolling fashion, the calculation error can be reduced, and the calculation results can be avoided from being inaccurate due to the large difference between the data contained in the first and second arrays.

[0060] In an optional embodiment, when performing RMS calculation, the first N data points, i.e., the data collected in the first N collections, should be removed. This removes unstable data from the initial collection phase, making the calculated RMS value more accurate.

[0061] In an optional embodiment, M can be 500 or a multiple of 500, or M can be 500 or 1000. This effectively reduces the computational load and difficulty, and improves computational efficiency.

[0062] Specifically, the RMS calculation formula is as follows:

[0063]

[0064] In the formula, n equals M, X i This represents the raw value.

[0065] In an optional embodiment, after the RMS value is calculated, the noise-calibrated data can be calibrated using MATLAB. The correspondence between the data before and after calibration can be added to the RMS calculation, which can further reduce the calculation error of the RMS value.

[0066] Specifically, using MATLAB to calibrate noise-calibrated data can remove outlier raw values. For example, conditions for outlier raw values ​​can be set and calibrated during calculation to reduce errors.

[0067] The beneficial effects of this disclosure are: by setting an independent filter for each acquisition channel, the acquisition accuracy is effectively improved; by performing noise calibration on the filtered signal, the influence of the device's own noise on the electromyography acquisition accuracy is removed; by setting a grounding module, the influence of external environmental factors such as static electricity is removed; the calibration and calculation processes are simple, and the computing power requirements of the calculation-related modules are low, which improves the calculation efficiency and makes the application of the acquisition method more flexible.

[0068] Example 2:

[0069] This application also provides a system for improving the accuracy of electromyography (EMG) acquisition, used to perform any of the aforementioned methods for improving EMG acquisition accuracy, including,

[0070] Acquisition module 11 is used for multi-channel acquisition of electromyographic signals;

[0071] The filtering module 12 is used to filter the acquired electromyographic signals. The filtering module includes several independent filter units, and each acquisition channel in the acquisition module is equipped with an independent filter unit.

[0072] Noise calibration module 13 is used to perform noise calibration on the filtered data using a two-point calibration method;

[0073] RMS calculation module 14 is used to perform RMS calculation on the noise-calibrated data.

[0074] In an optional embodiment, a grounding module is also included, which is used to ground the acquisition module.

[0075] In an optional embodiment, a storage module is also included, which is used to store the acquired electromyographic signals, filtered data, noise-calibrated data, and the results calculated by RMS.

[0076] In an optional embodiment, a communication module may also be included, which is used to send the data obtained after filtering by the filtering module 12 to the noise calibration module 13 for calibration.

[0077] The beneficial effects of this disclosure are that by setting an independent filter for each acquisition channel, the acquisition accuracy is effectively improved; by performing noise calibration on the filtered signal, the influence of the device's own noise on the electromyography acquisition accuracy is removed; by setting a grounding module, the influence of external environmental factors such as static electricity is removed; the calibration and calculation processes are simple, and the computing power requirements of the calculation-related modules are low, which improves the calculation efficiency and makes the application of the acquisition method more flexible.

[0078] The system provided in the above embodiments is illustrated only by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for improving the accuracy of electromyography (EMG) acquisition, characterized in that, include: The acquisition module acquires electromyographic signals through multiple channels; The acquired electromyographic signals are filtered, with an independent filter unit set for each acquisition channel; Noise calibration is performed on the filtered data. Perform RMS calculation on the noise-calibrated data; The noise calibration of the filtered data includes: Short-circuit the acquisition module. Test the equipment noise according to the preset frequency value and record the noise value when short-circuited; Use a signal generator to input a sine wave signal, test the device noise according to the preset RMS value, and record the noise value when the sine wave signal is input. Based on the noise value during short circuit and the noise value when inputting a sine wave signal, the filtered data is processed to remove the device noise value from the filtered data; When a sine wave signal is input, the preset RMS value is 100.

2. The method for improving the accuracy of electromyography (EMG) acquisition according to claim 1, characterized in that, The acquisition frequency of the multi-channel electromyography signal is 205Hz.

3. The method for improving the accuracy of electromyography (EMG) acquisition according to claim 1, characterized in that, When the acquisition module is short-circuited, the preset frequency value is 205Hz.

4. The method for improving the accuracy of electromyography (EMG) acquisition according to claim 1, characterized in that, The RMS calculation of the noise-calibrated data includes: When the number of raw values ​​is M, the M raw values ​​are treated as an array, and an RMS value is calculated using the RMS calculation formula. When the number of raw values ​​is M+1, remove the first raw value, and treat the second to the M+1th raw values ​​as an array. Calculate the next RMS value using the RMS calculation formula.

5. A system for improving the accuracy of electromyography (EMG) acquisition, used to perform the method for improving the accuracy of EMG acquisition as described in any one of claims 1-4, characterized in that, include, The acquisition module is used for multi-channel acquisition of electromyographic signals; The filtering module is used to filter the acquired electromyographic signals. The filtering module includes several independent filter units, and each acquisition channel in the acquisition module is equipped with an independent filter unit. The noise calibration module is used to calibrate the noise in the filtered data. The RMS calculation module is used to perform RMS calculations on the noise-calibrated data.

6. The system for improving the accuracy of electromyography (EMG) acquisition according to claim 5, characterized in that, It also includes a grounding module, which is used to ground the acquisition module.

7. The system for improving the accuracy of electromyography (EMG) acquisition according to claim 5, characterized in that, It also includes a storage module, which is used to store the acquired electromyographic signals, filtered data, noise-calibrated data, and the results calculated by RMS.

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