Electromyographic signal acquisition device

Abstract

The invention relates to the technical field of signal processing, in particular to an electromyographic signal acquisition device. The device comprises a signal sensing module which comprises at least one signal sensing unit, and the signal sensing units are used for collecting simulated electromyographic signals; the analog signal high-speed acquisition module is used for carrying out oversampling on the analog electromyographic signals based on a target sampling rate to obtain first digital electromyographic signals, and the target sampling rate is larger than ten times of the highest frequency of the analog electromyographic signals; and the digital signal processing module is used for controlling a digital filter to filter the first digital electromyographic signal to obtain a second digital electromyographic signal, and outputting a third digital electromyographic signal corresponding to the second digital electromyographic signal in a downsampling output mode. By adopting the scheme, the noise of electromyographic signal acquisition can be reduced, a digital processing method is used for replacing an analog processing circuit, and the flexibility and portability of the electromyographic signal acquisition device during use are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of signal processing technology, and in particular to a myoelectric signal acquisition device. Background Art

[0002] Current electromyographic signal acquisition devices usually use analog filtering circuits before acquisition, which adds active devices. Especially when the number of acquisition channels is large, the circuit is bulky and inevitably introduces noise. Moreover, the circuit has poor flexibility after welding, making it inconvenient to change system parameters and only processes a specific frequency range. In addition, in order to obtain higher measurement accuracy, there is a high voltage amplification in the front-end circuit, which is prone to limiting at the output end. In addition, the use of analog amplification circuits increases the size of the hardware circuit and reduces the portability of the electromyographic signal acquisition device. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the purpose of the present disclosure is to propose an electromyographic signal acquisition device to reduce the noise of electromyographic signal acquisition, use digital processing methods instead of analog processing circuits, and improve the flexibility and portability of the electromyographic signal acquisition device when used.

[0005] To achieve the above objectives, an embodiment of the present disclosure provides an electromyographic signal acquisition device, comprising:

[0006] A signal sensing module, comprising at least one signal sensing unit, wherein the signal sensing unit is used to collect analog electromyographic signals;

[0007] an analog signal high-speed acquisition module, configured to oversample the analog electromyographic signal based on a target sampling rate to obtain a first digital electromyographic signal, wherein the target sampling rate is greater than ten times the highest frequency of the analog electromyographic signal;

[0008] The digital signal processing module is used to control the digital filter to filter the first digital electromyographic signal to obtain a second digital electromyographic signal, and output a third digital electromyographic signal corresponding to the second digital electromyographic signal in a down-sampling output manner.

[0009] Optionally, the digital signal processing module is used to control a digital filter to filter the first digital electromyographic signal to obtain a second digital electromyographic signal, specifically to:

[0010] Controlling a low-pass digital filter to perform low-pass filtering on the first digital electromyographic signal to obtain a fourth digital electromyographic signal;

[0011] The bandpass digital filter is controlled to perform bandpass filtering on the fourth digital electromyographic signal to obtain a second digital electromyographic signal.

[0012] Optionally, the passband cutoff frequency corresponding to the low-pass digital filter is 1000 Hz, and the stopband cutoff frequency is 1200 Hz.

[0013] Optionally, the lower passband cutoff frequency corresponding to the bandpass digital filter is 20 Hz, the upper passband cutoff frequency is 500 Hz, the lower stopband cutoff frequency is 5 Hz, and the upper stopband cutoff frequency is 600 Hz.

[0014] Optionally, before the controlling digital filter performs filtering processing on the first digital electromyographic signal to obtain a second digital electromyographic signal, the digital signal processing module is further configured to:

[0015] Loading filter coefficients, wherein the filter coefficients are obtained by quantizing initial filter coefficients, and the initial filter coefficients are obtained by designing a Kaiser window function;

[0016] A digital filter is constructed based on the filter coefficients.

[0017] Optionally, quantizing the initial filter coefficients includes:

[0018] Normalizing the initial filter coefficients to obtain normalized filter coefficients;

[0019] Obtaining an integer factor, and multiplying the normalized filter coefficient by the integer factor to obtain a multiplied filter coefficient;

[0020] The multiplied filter coefficients are truncated to obtain the filter coefficients.

[0021] Optionally, when the digital signal processing module is configured to output the third digital electromyographic signal corresponding to the second digital electromyographic signal in a down-sampling output manner, it is specifically configured to:

[0022] Every preset number of data points, a data point is extracted from the second digital electromyographic signal to obtain and output a third digital electromyographic signal.

[0023] Optionally, the analog signal high-speed acquisition module includes at least one analog signal high-speed acquisition unit, and the analog signal high-speed acquisition unit corresponds one-to-one to the signal sensing unit.

[0024] Optionally, the device further comprises:

[0025] The data transmission and acquisition terminal is used to receive and store the third digital myoelectric signal.

[0026] Optionally, after receiving and storing the third digital myoelectric signal, the data transmission and acquisition terminal is further used to:

[0027] An integer factor corresponding to the third digital electromyographic signal is obtained, and data scaling processing is performed on the third digital electromyographic signal according to the integer factor to obtain and store a fourth digital electromyographic signal.

[0028] Optionally, the data transmission and acquisition terminal receives the third digital electromyographic signal output by the digital signal processing module through a network.

[0029] In summary, the electromyographic signal acquisition device provided by the present invention performs high-speed oversampling of electromyographic signals. Due to the large amount of oversampled data, the effective resolution of the collected electromyographic signals can be improved after digital signal processing, thereby achieving the effect of noise reduction, reducing high-frequency interference, making the detection of small-amplitude signals more accurate, and ensuring that the processed electromyographic signals are real and usable; secondly, when oversampling and digital signal processing are used, no analog filtering is required, no signal amplification is required, and the analog amplification circuit can be ignored, which can greatly reduce the volume of the hardware circuit and improve the portability of the electromyographic signal acquisition device; in addition, digital filtering is completed by the digital filter in the digital signal processing module, and the digital filter parameters can be changed online and are flexibly adjustable without replacing any hardware circuits, thereby reducing hardware costs and volume, improving reusability, and greatly increasing the flexibility and adaptability of the device.

[0030] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A schematic structural diagram of an electromyographic signal acquisition device provided by one embodiment of the present disclosure;

[0033] Figure 2 A schematic structural diagram of a myoelectric signal acquisition device provided by another embodiment of the present disclosure;

[0034] Figure 3 A schematic diagram of the operating timing of an ADS1256 provided in an embodiment of the present disclosure;

[0035] Figure 4 A flowchart of the ADS1256 provided in an embodiment of the present disclosure;

[0036] Figure 5A schematic diagram of a network connection of an FPGA provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0038] It's important to note that when the central nervous system transmits neurons with movement intention to the relevant muscles, it activates surface electromyography (EMG) signals in the corresponding muscles. This signal can reflect the body's movement intention. As a critical physiological signal, surface electromyography (EMG) contains a wealth of information about limb movement. EMG signals appear 10-200 milliseconds before the onset of limb movement. They can be used to determine the patient's active movement intention and accelerate the response of rehabilitation robots to the patient's movement intention. Currently, they are one of the most suitable data streams for human-machine interaction and can help rehabilitation robots develop proactive rehabilitation strategies. For rehabilitation robots, quickly and accurately identifying the patient's movement intention using surface electromyography (SEMG) signals is fundamental to developing proactive rehabilitation strategies. SEMG-based intention recognition primarily involves five stages: surface electromyography (EMG) signal acquisition, signal processing, feature extraction, classification training, and model validation. SEMG acquisition and processing are key steps in movement intention recognition. Because of the high signal quality requirements for intention recognition, higher demands are placed on the EMG signal acquisition device.

[0039] The present disclosure is described in detail below with reference to specific embodiments.

[0040] Figure 1 This is a schematic diagram of the structure of an electromyographic signal acquisition device provided by an embodiment of the present disclosure. Figure 1 As shown, the electromyographic signal acquisition device includes:

[0041] A signal sensing module includes at least one signal sensing unit, and the signal sensing unit is used to collect analog electromyographic signals;

[0042] an analog signal high-speed acquisition module, configured to oversample the analog electromyographic signal based on a target sampling rate to obtain a first digital electromyographic signal, wherein the target sampling rate is greater than ten times the highest frequency of the analog electromyographic signal;

[0043] The digital signal processing module is used to control the digital filter to filter the first digital electromyographic signal to obtain a second digital electromyographic signal, and output a third digital electromyographic signal corresponding to the second digital electromyographic signal in a down-sampling output manner.

[0044] According to some embodiments, the characteristics of the analog electromyographic signal are as follows: frequency 20 to 500 Hz, low amplitude, and susceptibility to noise. The sampling theorem theoretically indicates that in order to reconstruct the input analog signal, the sampling frequency fs must be greater than or equal to twice the highest frequency fm of the input analog signal, that is: fs>fm×2. In other words, when sampling the analog electromyographic signal, without considering the influence of noise, a sampling rate of 1000sps can meet the requirements. In this case, in order to avoid interference from high-frequency signals, the original analog processing method usually adds an analog filtering circuit at the front end of the acquisition circuit to reduce the interference of high-frequency signals on sampling and subsequent operations.

[0045] In some embodiments, oversampling is a method that sacrifices sampling speed in exchange for effective resolution. The relationship between the signal-to-noise ratio (SNR) and the oversampling ratio (OSR) is as follows:

[0046] SNR=6.02N+1.76+10log(OSR)

[0047] Where N is the number of sampling points. When OSR is 4T, the following formula is obtained:

[0048] SNR=6.02N+1.76+10log(4 T )

[0049] SNR=6.02N+1.76+T*10*0.602

[0050] SNR=6.02(N+T)+1.76

[0051] That is, every 4 increase in OSR T By increasing the oversampling rate by 30, the effective resolution can be increased by 2 bits. For example, when the Nyquist sampling frequency is 1Ksps and the actual sampling rate is 30Ksps, the oversampling rate is 30, and theoretical calculations show that the effective resolution can be increased by 2 bits. Therefore, by performing high-speed oversampling on the EMG signal, that is, the target sampling rate is greater than ten times the highest frequency of the analog EMG signal, the effective resolution of the collected EMG signal can be improved after digital signal processing due to the large amount of oversampled data. This achieves the effect of noise reduction, reduces high-frequency interference, and makes the detection of small-amplitude signals more accurate. This ensures that the processed EMG signal is authentic and usable, thereby improving the accuracy of subsequent intent recognition and terminal control, laying the foundation for subsequent EMG-based intent classification, intent recognition, and terminal control.

[0052] Secondly, when oversampling and digital signal processing are used, analog filtering is no longer necessary. Instead, digital filtering is performed by the digital filter in the digital signal processing module. The filter frequency characteristics of the digital filter, i.e., the digital filter parameters, can be modified online and flexibly adjusted without replacing any hardware circuits. This reduces hardware cost and size, improves reusability, and significantly increases the flexibility and adaptability of the device. Furthermore, when analog circuits are used for processing, a voltage amplifier module is required at the front end of the acquisition device to improve signal measurement accuracy. However, when oversampling and digital signal processing are used, signal amplification is not required, and the analog amplifier circuit can be omitted to achieve the same resolution. Therefore, by reducing the analog filtering and amplifier circuits, the hardware circuit size can be significantly reduced, improving the portability of the EMG signal acquisition device.

[0053] Optionally, Figure 2 This is a structural diagram of a myoelectric signal acquisition device provided by another embodiment of the present disclosure. Figure 2 As shown, the signal sensing unit includes a patch electrode, which serves as a sensor. After the signal passes through this unit, it can be collected by the next module.

[0054] According to some embodiments, the patch electrodes may be Ag / AgCl electrodes with high sensitivity and low noise to ensure accurate perception of electromyographic signals.

[0055] In some embodiments, the electrode layout, the number of patch electrodes, and the number of channels can be flexibly adjusted according to actual conditions to achieve multi-channel parallel high-speed sampling.

[0056] Alternatively, as Figure 2 As shown, the analog signal high-speed acquisition module includes at least one analog signal high-speed acquisition unit, and the analog signal high-speed acquisition unit corresponds to the signal sensing unit one by one.

[0057] It should be noted that when the same high-speed analog signal acquisition unit is used to simultaneously process analog EMG signals collected by multiple signal sensing units using the unit's internal channel conversion mode, the sampling rate will be reduced due to the time required for channel conversion. Therefore, by setting up a one-to-one correspondence between the high-speed analog signal acquisition units and the signal sensing units, each high-speed analog signal acquisition unit oversamples the analog EMG signals collected by the corresponding signal sensing unit to ensure the highest sampling rate.

[0058] In some embodiments, as Figure 2As shown, the analog signal high-speed acquisition unit can use the high-resolution, high-speed analog-to-digital converter (ADC) chip ADS1256. This is a 24-bit ADC chip with a maximum sampling rate of 30K, which can meet the requirements of oversampling frequency and ensure accurate signal conversion. It can be used to convert analog signals into digital signals, and then transmit them to the digital signal processing module for processing through the serial peripheral interface (SPI) bus.

[0059] According to some embodiments, a digital signal processing module can be used to drive an analog signal high-speed acquisition unit to oversample analog electromyographic signals. For example, the SPI method can be used to write signals and read data to the ADC in the ADS1256, where the main ADC function parameters can be configured as follows:

[0060] The programmable gain amplifier (PGA) is set to 1;

[0061] In order to have a wider analog signal input range, turn off the analog input buffer;

[0062] Turn off the digital filter;

[0063] The input is dual-channel differential input, and the rest of the settings remain the chip default.

[0064] Secondly, since ADS1256 is a serial input and output chip, the timing during operation needs to be strictly in accordance with the data sheet, such as Figure 3 The communication mode is similar to SPI mode 0. When designing the driver, it is necessary to determine a reasonable SPI clock high level width. The specific parameter calculation and setting are as follows:

[0065] The crystal frequency used is f CLKIN is 7.68MHz, so the main clock period τ CLKIN 1 / 7.68 = 0.13 μs; the target sampling rate is 30 Ksps, so the output data period τ DATA 1 / 30 = 0.033 μs;

[0066] The SCLK clock cycle is flexibly adjusted according to the transmission timing;

[0067] In the RDATA, RDATAC, and RREG commands, the minimum duration of the delay t6 from the last SCLK edge of DIN to the first SCLK rising edge of DOUT is 50τ. CLKIN =6.5μs;

[0068] t10 The minimum is t 10 =8τ CLKIN =1.04μs, used to enable The signal is always pulled low.

[0069] The minimum delay between RREG, WREG, and RDATA commands is t 11 =4τ CLKIN =0.52μs; the minimum delay between RDATAC and / SYNC command is t 11 =24τ CLKIN =3.12μs.

[0070] In some embodiments, Figure 4 This is a workflow diagram of ADS1256 provided by the embodiment of the present disclosure. Figure 4 As shown in the figure, after power-on, the ADS1256 enters the idle state IDLE, and then delays for 100 milliseconds (DELAY_100) for the device to complete initialization and other operations; after completion, it starts to wait for the falling edge of the DRDY signal (WAIT_DRDY), that is, waits for the sampling conversion to be completed; after completion, the RDATAC (continuous read) command is written to the ADC via SPI, and the flag bit flag is set to high. After completion, it immediately enters the corresponding delay (DELAY_t6); after the delay is completed, the 24-bit conversion data (RD_DATA) can be read from the DOUT interface. After the conversion is completed, it continues to enter the necessary delay (DELAY_t11) and waits for the next conversion.

[0071] Since the command written is a continuous read, there is no need to repeatedly write commands to the ADC. When a new DRDY falling edge arrives, there is no need to write commands again. After the delay, the converted data can be directly read from the DOUT interface.

[0072] Optionally, the digital signal processing module is used to control the digital filter to filter the first digital electromyographic signal to obtain the second digital electromyographic signal, specifically to:

[0073] Controlling a low-pass digital filter to perform low-pass filtering on the first digital electromyographic signal to obtain a fourth digital electromyographic signal;

[0074] The bandpass digital filter is controlled to perform bandpass filtering on the fourth digital electromyographic signal to obtain a second digital electromyographic signal.

[0075] According to some embodiments, since the frequency of the electromyographic signal is concentrated in the range of 20-500 Hz, the passband cutoff frequency corresponding to the low-pass digital filter can be 1000 Hz, and the stopband cutoff frequency can be 1200 Hz; the lower passband cutoff frequency corresponding to the bandpass digital filter can be 20 Hz, the upper passband cutoff frequency can be 500 Hz, the lower stopband cutoff frequency can be 5 Hz, and the upper stopband cutoff frequency can be 600 Hz.

[0076] In some embodiments, the first digital electromyographic signal is firstly subjected to low-pass filtering by a low-pass digital filter to filter out high-frequency noise in the first digital electromyographic signal; then, the fourth digital electromyographic signal is subjected to band-pass filtering by a band-pass digital filter to retain the main features of the electromyographic signal.

[0077] According to some embodiments, the digital signal processing module may construct a digital filter according to the filter coefficients by loading the filter coefficients.

[0078] In some embodiments, a digital filter is essentially a multiplication and addition operation of a digital sequence. The digital filter may be, for example, a finite impulse response (FIR) filter. For an M-order FIR filter, its mathematical expression in the time domain is as follows:

[0079]

[0080] Among them, x is the input sequence; y is the output sequence; h is the coefficient, also known as the tap weight.

[0081] According to some embodiments, a digital filter can be designed using a Karise window function to obtain initial filter coefficients. Subsequently, the initial filter coefficients are quantized to obtain the filter coefficients. Therefore, by quantizing the initial filter coefficients, using integer quantization can consume fewer computing resources and facilitate function implementation, compared to directly performing floating-point operations on decimals.

[0082] In some embodiments, the requirements for the window function include but are not limited to reducing the main lobe width as much as possible to shorten the transition band, reduce the amplitude of the maximum side lobe, concentrate the energy on the main lobe, increase the stopband attenuation, and obtain better filter characteristics.

[0083] In some embodiments, when quantizing the initial filter coefficients, the initial filter coefficients may be normalized to obtain normalized filter coefficients; integer factors may be obtained, and the normalized filter coefficients may be multiplied by the integer factors to obtain the multiplied filter coefficients; and the multiplied filter coefficients may be truncated to obtain the filter coefficients. Thus, all initial filter coefficients may be quantized to integers, with the decimal point of the quantized data being located to the right of the most significant bit.

[0084] The integer factor can be determined based on the required quantization bit width. The truncation processing methods include but are not limited to rounding, rounding up or down, conjugate rounding, random rounding, etc.

[0085] Alternatively, the digital signal processing module can utilize a field programmable gate array (FPGA), for example. FPGAs are parallel computing chips used for real-time processing and analysis of digital signals. They can process multiple signal streams in parallel, increasing data processing speed and making them ideally suited for digital signal processing tasks. The FPGA's parallel computing capabilities enable high-speed, real-time multi-channel digital filtering, reducing the impact of noise.

[0086] According to some embodiments, the filter coefficients may be loaded into a FIR Compiler IP in an FPGA, and then corresponding parameters may be set to construct a desired digital filter in the FPGA.

[0087] Optionally, when the digital signal processing module is configured to output the third digital electromyographic signal corresponding to the second digital electromyographic signal in a down-sampling output manner, it is specifically configured to:

[0088] Every preset number of data points, a data point is extracted from the second digital electromyographic signal to obtain and output a third digital electromyographic signal.

[0089] For example, when the target output frequency is 1 kHz, it is necessary to reduce 30,000 data points per second in the second digital electromyographic signal to 1,000 data points per second, that is, extract one filtered data point every 30 data points.

[0090] Optionally, the electromyographic signal acquisition device further includes:

[0091] The data transmission and acquisition terminal is used to receive and store the third digital myoelectric signal.

[0092] According to some embodiments, the data transmission and collection terminal can run on an embedded Linux system. Figure 2 As shown, the data transmission and collection terminal can be, for example, a host computer.

[0093] In some embodiments, the data transmission and acquisition terminal can also display the processed electromyographic signal data and provide remote data download and analysis functions.

[0094] It should be noted that, since the initial filter coefficients are quantized, data scaling is performed during the filtering process, and the number of bits of the second digital myoelectric signal after filtering will increase accordingly, such as Figure 2 As shown in the figure, the number of bits of the first digital EMG signal is 24 bits, and the number of bits of the second and third digital EMG signals is 40 bits. Therefore, if you want to display the actual data on the data transmission and acquisition terminal, you need to scale the data back. That is, after receiving and storing the third digital EMG signal, the data transmission and acquisition terminal is also used to:

[0095] Obtaining an integer factor corresponding to the third digital myoelectric signal, performing data scaling processing on the third digital myoelectric signal according to the integer factor, and obtaining and storing a fourth digital myoelectric signal;

[0096] In response to receiving the display instruction for the digital electromyographic signal, the data transmission and collection terminal may display the fourth digital electromyographic signal.

[0097] In some embodiments, since the number of bits of the third digital myoelectric signal increases to 40 bits and the data transmission volume is large, the data transmission and acquisition terminal can receive the third digital myoelectric signal output by the digital signal processing module through the network. For example, Realtek's RTL8211 Ethernet PHY chip can be used to provide support for Ethernet connection, supporting MII, GMII, and RGMII interfaces. The hardware connection diagram with the FPGA is shown in FIG. Figure 5 shown.

[0098] In summary, the electromyographic signal acquisition device provided by the embodiment of the present disclosure uses FPGA to control a multi-channel ADC for high-precision sampling and digital signal processing. Among them, FPGA controls the high-speed and high-precision ADC to complete high-speed oversampling of electromyographic signals, FPGA completes real-time signal processing, completes single-stage or multi-stage bandpass filtering, etc., which can replace the original analog filtering circuit, can effectively solve the problem of insufficient resolution of the acquisition device, ensure the quality of EMG signals, and the dynamic range of the acquired signals is large and the signal accuracy is high, which can provide higher quality data for subsequent feature extraction, thereby increasing the accuracy of intention recognition; secondly, it can replace most analog processing circuits, further reduce the volume of hardware circuits, realize miniaturized portable measurement, improve the flexibility and intelligence level of exoskeleton equipment, and can be applied to electromyographic signal acquisition scenarios with high acquisition rate, large number of channels and complex calculations.

[0099] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this disclosure are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0100] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0101] This disclosure contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0102] The acquisition, transmission, storage, use, and processing of data in the technical solution disclosed herein are in compliance with the relevant provisions of national laws and regulations.

[0103] It should be noted that in the embodiments of the present disclosure, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary and their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0104] In the descriptions of the aforementioned embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0105] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0106] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0107] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0108] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0109] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0110] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0111] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. A person of ordinary skill in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A myoelectric signal acquisition device, characterized in that: include: A signal sensing module, comprising at least one signal sensing unit, wherein the signal sensing unit is used to collect analog electromyographic signals; an analog signal high-speed acquisition module, configured to oversample the analog electromyographic signal based on a target sampling rate to obtain a first digital electromyographic signal, wherein the target sampling rate is greater than ten times the highest frequency of the analog electromyographic signal; The digital signal processing module is used to control the digital filter to filter the first digital electromyographic signal to obtain a second digital electromyographic signal, and output a third digital electromyographic signal corresponding to the second digital electromyographic signal in a down-sampling output manner.

2. The device according to claim 1, characterized in that The digital signal processing module is used to control the digital filter to filter the first digital electromyographic signal to obtain the second digital electromyographic signal, specifically for: Controlling a low-pass digital filter to perform low-pass filtering on the first digital electromyographic signal to obtain a fourth digital electromyographic signal; The bandpass digital filter is controlled to perform bandpass filtering on the fourth digital electromyographic signal to obtain a second digital electromyographic signal.

3. The device according to claim 2, characterized in that The passband cutoff frequency of the low-pass digital filter is 1000 Hz, and the stopband cutoff frequency is 1200 Hz; The lower passband cutoff frequency corresponding to the bandpass digital filter is 20 Hz, the upper passband cutoff frequency is 500 Hz, the lower stopband cutoff frequency is 5 Hz, and the upper stopband cutoff frequency is 600 Hz.

4. The device according to claim 1, characterized in that Before the control digital filter performs filtering processing on the first digital electromyographic signal to obtain the second digital electromyographic signal, the digital signal processing module is further used to: Loading filter coefficients, wherein the filter coefficients are obtained by quantizing initial filter coefficients, and the initial filter coefficients are obtained by designing a Kaiser window function; A digital filter is constructed based on the filter coefficients.

5. The device according to claim 4, characterized in that The quantizing of the initial filter coefficients comprises: Normalizing the initial filter coefficients to obtain normalized filter coefficients; Obtaining an integer factor, and multiplying the normalized filter coefficient by the integer factor to obtain a multiplied filter coefficient; The multiplied filter coefficients are truncated to obtain the filter coefficients.

6. The device according to claim 1, characterized in that When the digital signal processing module is used to output the third digital myoelectric signal corresponding to the second digital myoelectric signal in a down-sampling output manner, it is specifically used to: Every preset number of data points, a data point is extracted from the second digital electromyographic signal to obtain and output a third digital electromyographic signal.

7. The device according to claim 1, characterized in that The analog signal high-speed acquisition module includes at least one analog signal high-speed acquisition unit, and the analog signal high-speed acquisition unit corresponds to the signal sensing unit one by one.

8. The device according to claim 1, characterized in that The device further comprises: The data transmission and acquisition terminal is used to receive and store the third digital myoelectric signal.

9. The device according to claim 8, characterized in that After receiving and storing the third digital myoelectric signal, the data transmission and acquisition terminal is further used to: An integer factor corresponding to the third digital electromyographic signal is obtained, and data scaling processing is performed on the third digital electromyographic signal according to the integer factor to obtain and store a fourth digital electromyographic signal.

10. The device according to claim 8, characterized in that The data transmission and acquisition terminal receives the third digital myoelectric signal output by the digital signal processing module through a network.

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