Digital noise reduction method based on muscle electrical signals and related devices

Abstract

The application relates to the technical field of signal processing, in particular to a digital noise reduction method based on a muscle electrical signal and related equipment. The method comprises the following steps: acquiring an original muscle electrical signal; reducing the ADC value of the acquired original muscle electrical signal according to an amplification ratio to obtain a restored amplification signal; performing filter processing on the restored amplification signal through a fourth-order Butterworth band-pass filter to obtain a first filter signal; performing filter processing on the first filter signal through a double-order notch filter to remove power frequency noise and obtain a second filter signal; and performing noise reduction processing on the second filter signal through short-time Fourier transform with a Hamming window to obtain a target noise reduction signal. The application can reduce phase delay, improve signal timing accuracy, reduce calculation overhead, and enhance noise suppression capability.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a digital noise reduction method and related equipment based on electromyography (EMG) signals. Background Technology

[0002] In numerous fields such as medicine, sports science, and human-computer interaction, surface electromyography (EMG) acquisition products, as non-invasive devices for measuring and analyzing electrical signals of muscle activity, have emerged and gained widespread application. Currently, the mainstream technology employs a multi-stage cascaded filtering architecture. This architecture first uses a high-pass filter (10-20Hz) to eliminate baseline drift, then a band-pass filter (20-500Hz) to suppress high and low frequency noise, and finally adds a power frequency notch filter (50 / 60Hz) to remove power supply interference. Many devices, such as the Delsys TRIGNO system, utilize this widely used approach. However, this seemingly mature technology has some significant drawbacks. On the one hand, the cascaded multi-stage filters cause signal time delays to accumulate, sometimes exceeding 30ms. This severely distorts the timing characteristics of muscle activation, posing a significant obstacle for applications relying on precise timing analysis. On the other hand, the stopband of the band-pass filter already covers 0-20Hz. In this case, the independently configured high-pass filter not only fails to provide additional effective support but also incurs approximately 20% additional computational overhead, undoubtedly reducing system efficiency. In addition, the Q value of traditional single-stage notch filters is limited, generally less than 2, which makes their suppression effect on power frequency harmonics (100 / 150Hz) insufficient, and they cannot completely eliminate the impact of such interference on signal quality.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a digital noise reduction method and related equipment based on electromyography (EMG) signals, which can reduce phase delay, improve signal timing accuracy, reduce computational overhead, and enhance noise suppression capabilities.

[0005] To achieve the above objectives, one aspect of this application proposes a digital noise reduction method based on electromyography (EMG) signals, the method comprising the following steps:

[0006] Obtain raw muscle electrical signals;

[0007] The ADC value of the acquired original electromyography signal is reduced according to the amplification ratio to obtain the restored amplified signal;

[0008] The restored and amplified signal is filtered by a fourth-order Butterworth bandpass filter to obtain the first filtered signal.

[0009] The first filtered signal is filtered by a second-order notch filter to remove power frequency noise and obtain the second filtered signal.

[0010] The second filtered signal is denoised by applying a Hamming window to a short-time Fourier transform to obtain the target denoised signal.

[0011] In some embodiments, the fourth-order Butterworth bandpass filter is used to filter out noise other than the main energy region of the electromyography signal, and the frequency band range of the fourth-order Butterworth bandpass filter includes 20Hz to 500Hz.

[0012] In some embodiments, the fourth-order Butterworth bandpass filter includes a direct-type II structure.

[0013] In some embodiments, the dual-order notch filter includes dual second-order notch filters.

[0014] In some embodiments, the Q factor of the dual second-order notch filter is adjustable, and the Q factor includes 4.

[0015] In some embodiments, the short-time Fourier transform with Hamming window is used to eliminate data distortion between different data segments caused by Fourier transform of segmented data.

[0016] To achieve the above objectives, another aspect of this application proposes a digital noise reduction device based on electromyography (EMG) signals, the device comprising:

[0017] The acquisition module is used to acquire raw muscle electrical signals;

[0018] The reduction and amplification module is used to reduce the ADC value of the acquired original electromyographic signal according to the amplification ratio to obtain the reduced and amplified signal;

[0019] The first filtering module is used to filter the restored and amplified signal through a fourth-order Butterworth bandpass filter to obtain a first filtered signal;

[0020] The second filtering module is used to filter the first filtered signal through a second-order notch filter to remove power frequency noise and obtain the second filtered signal.

[0021] The target noise reduction module is used to perform noise reduction processing on the second filtered signal through a short-time Fourier transform with a Hamming window to obtain the target noise-reduced signal.

[0022] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0023] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0024] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0025] The embodiments of this application include at least the following beneficial effects: This application provides a digital noise reduction method and related equipment based on electromyography (EMG) signals. This scheme replaces redundant high-pass filters with a fourth-order Butterworth bandpass filter, reducing phase delay by 58%, significantly reducing the signal time delay superposition problem caused by multi-stage filter cascading, and effectively avoiding severe distortion of muscle activation timing characteristics. Since the stopband of the fourth-order Butterworth bandpass filter covers the 0-20Hz frequency band that originally required independent high-pass filters, there is no need to set up additional high-pass filters, reducing additional computational overhead by about 20%, improving system operating efficiency, and making the device smoother when processing EMG signals in real time. The dual second-order notch filter compresses the power frequency suppression bandwidth to ±5Hz through an adjustable Q factor and improves the attenuation of 100Hz harmonics to -40dB. Compared with traditional single-stage notch filters, it significantly improves the suppression effect on power frequency and its harmonics, effectively removes noise such as power supply interference, ensures the purity of EMG signals, and enables human-computer interaction devices to more accurately identify and respond to muscle activity commands. Attached Figure Description

[0026] Figure 1 This is a flowchart of a digital noise reduction method based on electromyography (EMG) signals provided in an embodiment of this application;

[0027] Figure 2 This is a schematic diagram of the amplitude response curve of a fourth-order Butterworth filter;

[0028] Figure 3 This is a schematic diagram of the structure of a first-order fourth-order Butterworth filter;

[0029] Figure 4 This is a schematic diagram of the structure of a second- or fourth-order Butterworth filter;

[0030] Figure 5 This is a schematic diagram of the amplitude response curve of a second-order notch filter;

[0031] Figure 6 This is a schematic diagram of a second-order notch filter.

[0032] Figure 7 This is a schematic diagram of a digital noise reduction device based on electromyography (EMG) signals.

[0033] Figure 8 This is a schematic diagram of the hardware structure of an electronic device. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application 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 merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0035] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0036] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0038] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0039] ADC value: ADC stands for Analog-to-Digital Converter. The ADC value is the numerical result of converting a continuously changing analog signal into a discrete digital signal. In wireless surface electromyography (EMG) acquisition, it represents the value obtained by the acquisition device after digitizing the original EMG analog signal, and is the basic data for subsequent signal processing.

[0040] Q-factor: In a filter, the Q-factor reflects its frequency selectivity. The higher the Q-value, the narrower the filter's bandwidth, the stronger its selectivity for signals at specific frequencies, and the stronger its ability to suppress out-of-band signals.

[0041] Direct-type II structure: This is a type of filter implementation. Taking a fourth-order Butterworth filter as an example, this structure is mathematically equivalent to a low-pass filter and a high-pass filter in series, but it can reduce numerical errors, lower computational resource consumption, and improve filtering efficiency and stability.

[0042] sEMG: or surface muscle electrical signal, is a weak electrical signal generated and conducted to the skin surface during muscle activity.

[0043] Figure 1 This is an optional flowchart of a digital noise reduction method based on electromyography (EMG) signals provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S100 to S500.

[0044] Step S100: Obtain raw muscle electrical signals;

[0045] Step S200: The ADC value of the acquired raw electromyography signal is reduced according to the amplification ratio to obtain the restored amplified signal;

[0046] Step S300: The restored amplified signal is filtered by a fourth-order Butterworth bandpass filter to obtain the first filtered signal;

[0047] Step S400: The first filtered signal is filtered by a second-order notch filter to remove power frequency noise and obtain the second filtered signal.

[0048] In step S500, the second filtered signal is denoised by a short-time Fourier transform with a Hamming window to obtain the target denoised signal.

[0049] Steps S100 to S500 as shown in the embodiments of this application sequentially pass through fourth-order Butterworth bandpass filtering, double second-order notch filtering, and short-time Fourier transform noise reduction. Each step is closely connected, making full use of the advantages of different filters to form a highly efficient and complete electromyography signal processing scheme. It can comprehensively remove various types of noise and provide high-quality signal data for subsequent muscle function assessment, analysis, and other applications, which has important practical value in multiple fields.

[0050] In some embodiments, in step S100, the raw muscle electrical signals are acquired using specialized acquisition devices, which typically include electrode pads. These electrode pads are placed on specific muscle areas on the surface of the skin, and the weak electrical signals generated by muscle activity are detected by the electrode pads. The internal circuitry of the acquisition device performs preliminary amplification and conditioning of these weak signals to meet the needs of subsequent processing.

[0051] In some embodiments, in step S200, during the wireless surface electromyography (EMG) signal processing, the ADC value of the EMG signal acquired by the acquisition device is processed. The ADC value is the value obtained after converting the analog EMG signal into a digital signal. Since the EMG signal may have been amplified during signal acquisition to meet subsequent processing or transmission requirements, this step requires reducing the ADC value according to the previous amplification ratio to obtain a restored amplified signal. The purpose of this step is to restore the signal to a state close to its original state, providing accurate basic data for subsequent filtering and other processing. For example, if the EMG signal was amplified 100 times during acquisition, then in this step, the ADC value needs to be divided by 100 to restore information such as the amplitude of the EMG signal that is close to the actual signal.

[0052] In some embodiments, in step S300, such as Figure 2 , Figure 3 and Figure 4 As shown, Figure 2 This is a schematic diagram of the amplitude response curve of a fourth-order Butterworth filter. Figure 3 and Figure 4 This is a schematic diagram of the structure of the first and second fourth-order Butterworth filters. Figure 2 The horizontal axis represents frequency (Hz), and the vertical axis represents amplitude (dB). The curve shows that the filter amplitude is relatively stable in the frequency range of 20Hz to 450Hz, indicating that signals can pass through this band relatively smoothly. This band corresponds to the main energy region of sEMG. However, the amplitude decays rapidly in the frequency ranges below 20Hz and above 450Hz, meaning that noise in these bands is effectively filtered out. This characteristic allows a fourth-order Butterworth bandpass filter to replace redundant high-pass filters, preserving the effective frequency band while reducing phase delay. Measured delays are less than 12ms, significantly improving the efficiency and accuracy of signal processing. Figure 3 and Figure 4 The fourth-order Butterworth bandpass filter in the design employs a direct-type II structure, which helps reduce numerical errors. Figure 3 and Figure 4 It contains multiple adders, multipliers, and delay units. These components are interconnected, forming the operational structure of the filter. Mathematically, this structure is equivalent to a series connection of a low-pass and a high-pass filter, but it is more efficient in actual operation. Through this design, the filter can effectively filter the input signal, playing a crucial role in removing noise outside the main energy region of sEMG, laying a solid foundation for subsequent signal processing steps.

[0053] Specifically, the amplified signal is filtered using a fourth-order Butterworth bandpass filter. The amplitude-frequency response of a fourth-order Butterworth filter is relatively flat in the passband and attenuates more sharply in the stopband. The function of a bandpass filter is to allow signals within a specific frequency band to pass while suppressing signals outside that band. Here, the frequency range of the fourth-order Butterworth bandpass filter is set to 20Hz-500Hz because the main energy region of sEMG is concentrated in this frequency band. This filter can remove noise outside the main energy region of sEMG, such as some low-frequency baseline drift and high-frequency interference signals. Furthermore, the fourth-order Butterworth bandpass filter adopts a direct-type II structure, which reduces numerical errors and improves the accuracy and stability of filtering. Mathematically, it is equivalent to a series connection of low-pass and high-pass filters, but compared to using low-pass and high-pass filters separately in series, its computational efficiency is higher, reducing computational resource consumption while maintaining filtering effectiveness. After this filtering step, the filtered signal is obtained, which mainly retains the effective components of sEMG, and the noise is initially suppressed.

[0054] In some embodiments, in step S400, such as Figure 5 and Figure 6 As shown, Figure 5 This is a schematic diagram of the amplitude response curve of a second-order notch filter. Figure 6 This is a schematic diagram of a second-order notch filter. Figure 5 The horizontal axis represents frequency (Hz), and the vertical axis represents amplitude (dB). From Figure 5As can be seen, the filter amplitude at approximately 50Hz exhibits a sharp and significant attenuation, while remaining relatively stable in other frequency ranges. This indicates that the second-order notch filter effectively suppresses power frequency noise, typically at 50Hz or 60Hz. Power frequency noise is a common source of interference in electromyography (EMG) signal processing, significantly impacting signal quality. By using this second-order notch filter, the interference of power frequency noise can be significantly reduced while preserving the effective components of the EMG signal, thereby improving its purity and providing a more reliable data foundation for subsequent signal analysis and processing. For example, in practical applications, after processing the EMG signal using a fourth-order Butterworth bandpass filter, further removal of power frequency noise using this second-order notch filter allows the final denoised signal to more accurately reflect the electrical signal characteristics of muscle activity. Figure 6 The second-order notch filter in the image consists of multiple operational amplifiers (represented by triangles), adders (represented by circles), and delay units. The filter is constructed by interconnecting various components. This structural design allows the filter to deeply attenuate signals at specific frequencies of 50Hz or 60Hz (power frequency) while having minimal impact on signals at other frequencies. In the electromyography (EMG) signal processing flow, when the signal processed by the fourth-order Butterworth bandpass filter is input to this second-order notch filter, the filter can accurately identify and remove power frequency noise components. This filter structure effectively improves the quality of EMG signals, reduces noise interference, and provides strong support for subsequent short-time Fourier transform with a Hamming window for further noise reduction, ultimately resulting in a high-quality, denoised signal, thus meeting the needs for precise EMG signal analysis.

[0055] Specifically, the filtered signal is further filtered using a double-stage notch filter. Notch filters are primarily used to remove interference signals at specific frequencies; here, the double-stage notch filter is a double second-order notch filter. In practical applications, power frequency noise (50Hz or 60Hz) is a common source of interference during electromyography (EMG) signal acquisition, severely affecting the quality of the EMG signal. Due to its structural characteristics, the double second-order notch filter offers better stability and accuracy, and compared to traditional single-stage notch filters, it can more effectively remove power frequency noise. This step yields a signal with power frequency noise removed, significantly reducing the power frequency and related interference components and further improving signal purity.

[0056] In some embodiments, the Q factor of the dual second-order notch filter is adjustable. When the Q factor is 4, the power frequency suppression bandwidth can be compressed to ±5Hz. This feature is an optimized design addressing the shortcomings of traditional single-stage notch filters, which have limited Q values ​​(Q<2) and insufficient suppression of power frequency harmonics. By adjusting the Q factor to 4, the dual second-order notch filter can more accurately focus on power frequency noise and its adjacent frequency bands. While effectively compressing the suppression bandwidth, it enhances the targeted suppression effect on power frequency noise, providing a cleaner signal foundation for subsequent noise reduction processing using a short-time Fourier transform with a Hamming window.

[0057] In some embodiments, in step S500, a Hamming window-applied short-time Fourier transform is used to denoise the signal after power frequency noise removal. The short-time Fourier transform is a time-frequency analysis method that divides the signal into multiple short time segments and performs a Fourier transform on each segment to obtain information about the signal at different times and frequencies. However, when performing Fourier transforms on segmented data, data distortion may occur between different data segments, affecting the analysis results. A Hamming window is a window function that can weight the signal in each time segment, reducing problems such as spectral leakage caused by signal truncation, thereby minimizing data distortion between different data segments. In this step, by setting an appropriate threshold, noise components below the threshold are removed, retaining the effective signal components. After this step, a denoised signal is obtained, which has higher quality and can more accurately reflect the electrical signal characteristics of muscle activity, providing reliable data support for subsequent scientific analysis and human-computer interaction applications.

[0058] In some embodiments, the short-time Fourier transform with a Hamming window is used to eliminate data distortion between different data segments caused by Fourier transform of segmented data. This feature is designed to address the data segment distortion problem that may occur during real-time denoising due to Fourier transform of segmented data. By adding a Hamming window, the continuity and integrity of the signal can be guaranteed when denoising the signal after power frequency noise removal and eliminating noise below the threshold, providing a more reliable signal foundation for subsequent applications based on the denoised target signal.

[0059] Please see Figure 7 This application also provides a digital noise reduction device based on electromyography (EMG) signals, which can implement the above-described method. The device includes:

[0060] The acquisition module is used to acquire raw muscle electrical signals;

[0061] The reduction and amplification module is used to reduce the ADC value of the acquired raw electromyography signal according to the amplification ratio to obtain the reduced and amplified signal;

[0062] The first filtering module is used to filter the restored amplified signal through a fourth-order Butterworth bandpass filter to obtain the first filtered signal;

[0063] The second filtering module is used to filter the first filtered signal through a second-order notch filter to remove power frequency noise and obtain the second filtered signal.

[0064] The target noise reduction module is used to perform noise reduction processing on the second filtered signal through a short-time Fourier transform with a Hamming window to obtain the target noise-reduced signal.

[0065] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0066] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0067] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0068] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0069] The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0070] The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application.

[0071] The 803 input / output interface is used to implement information input and output.

[0072] The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0073] Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804);

[0074] The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0075] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0076] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0077] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0078] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0079] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0080] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0081] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0084] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0085] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0087] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A digital noise reduction method based on electromyography (EMG) signals, characterized in that, The method includes the following steps: Obtain raw muscle electrical signals; The ADC value of the acquired original electromyography signal is reduced according to the amplification ratio to obtain the restored amplified signal; The restored and amplified signal is filtered by a fourth-order Butterworth bandpass filter. The fourth-order Butterworth bandpass filter is used to filter out noise other than the main energy region of the electromyography signal. The frequency band range includes 20Hz to 500Hz. The fourth-order Butterworth bandpass filter includes a direct type II structure. The stopband of the fourth-order Butterworth bandpass filter covers 0–20Hz. The high-pass filter is removed to obtain the first filtered signal. The first filtered signal is filtered by a two-stage notch filter, which includes a second-stage notch filter with an adjustable Q factor of 4. When the Q factor of the second-stage notch filter is 4, the power frequency suppression bandwidth is compressed to ±5Hz, and the attenuation of 100Hz harmonics reaches -40dB. The two-stage notch filter suppresses 50Hz or 60Hz power frequency noise and removes power frequency noise to obtain the second filtered signal. The second filtered signal is denoised by applying a Hamming window to a short-time Fourier transform. Noise components below the threshold are removed by setting a threshold, and data distortion between different data segments caused by Fourier transform of segmented data is eliminated, thus obtaining the target denoised signal.

2. A digital noise reduction device based on electromyography (EMG) signals, characterized in that, The device includes: The acquisition module is used to acquire raw muscle electrical signals; The reduction and amplification module is used to reduce the ADC value of the acquired original electromyographic signal according to the amplification ratio to obtain the reduced and amplified signal; The first filtering module is used to filter the restored amplified signal through a fourth-order Butterworth bandpass filter. The fourth-order Butterworth bandpass filter is used to filter out noise other than the main energy region of the electromyography signal. The frequency band range includes 20Hz to 500Hz. The fourth-order Butterworth bandpass filter includes a direct type II structure. The stopband of the fourth-order Butterworth bandpass filter covers 0–20Hz. The high-pass filter is removed to obtain the first filtered signal. The second filtering module is used to filter the first filtered signal using a double-order notch filter. The double-order notch filter includes a double second-order notch filter. The Q factor of the double second-order notch filter is adjustable. The Q factor includes 4. When the Q of the double second-order notch filter is 4, the power frequency suppression bandwidth is compressed to ±5Hz, and the attenuation of 100Hz harmonics reaches -40dB. The double-order notch filter suppresses 50Hz or 60Hz power frequency noise, removes power frequency noise, and obtains the second filtered signal. The target noise reduction module is used to perform noise reduction processing on the second filtered signal by adding a Hamming window and performing a short-time Fourier transform. By setting a threshold, noise components below the threshold are removed, and data distortion between different data segments caused by Fourier transform of segmented data is eliminated to obtain the target noise reduction signal.

3. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of claim 1.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of claim 1.

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