Adaptive frequency adjustment and muscle relaxation intensity control method and system for fascia gun

Abstract

This application relates to the field of intelligent massage device control technology, and provides a method and system for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun. By receiving pressure values ​​from the massage head in contact with the human body collected by a pressure sensor, electromyographic signals from the target muscle group collected by an electromyography (EMG) sensor, and fascia gun motion posture data collected by an accelerometer, the motor rotation frequency range and corresponding output torque adjustment coefficient are obtained. Based on the motor rotation frequency range, a target adjustment frequency is determined within a preset safe frequency range, and a motor drive control signal is generated according to the output torque adjustment coefficient. The motor drive circuit controls the motor to operate at the target adjustment frequency. If the EMG sensor continuously collects EMG signals from the target muscle group showing a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is decreased, forming a gradual adjustment of muscle relaxation intensity. This application constructs a complete technical system for adaptive control of a fascia gun.

Description

Technical Field

[0001] This application relates to the field of intelligent massage equipment control technology, and in particular to a method and system for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun. Background Technology

[0002] Fascia guns, as common muscle relaxation devices, rely on a motor-driven high-frequency vibration of the massage head to relieve muscle tension. However, current fascia gun technology generally uses manual adjustment of preset levels for frequency and pressure control, which has the following significant drawbacks:

[0003] Lack of muscle state perception: Traditional solutions rely solely on the user's subjective judgment to select intensity levels, failing to obtain real-time physiological parameters such as the actual stiffness and fatigue level of the target muscle group, resulting in insufficient adjustment precision. For example, stiffer muscles require higher frequency stimulation, while fatigued muscles require gentle relaxation, but current technologies lack methods for quantitatively analyzing muscle state.

[0004] The current equipment lacks a systematic linkage between massage head pressure, motor frequency, and torque output. For example, when the pressing angle is tilted, the actual effective component of the contact pressure changes, but the equipment cannot automatically correct this, which may lead to pressure overload or reduced relaxation effect. Furthermore, pressure adjustment and frequency adjustment are independent of each other and have not formed a coordinated control logic based on muscle physiological characteristics.

[0005] The safety and comfort mechanisms are oversimplified: they only have basic overload protection (such as pressure switch power cut-off) and lack progressive adjustment strategies for the muscle relaxation process. For example, when the muscles adapt more during relaxation (electromyographic signals weaken), the intensity cannot be dynamically reduced to avoid overstimulation, or only a single down-level measure is taken when the pressure is abnormal, without graded protection based on the muscle fatigue state.

[0006] In the existing technology, there is no technical solution that uses electromyography signals to analyze muscle stiffness and fatigue, integrates pressure and posture data to build an adaptive adjustment model, and achieves coordinated control of frequency, torque, and pressure. Summary of the Invention

[0007] This application provides a method and system for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun, aiming to solve the problems in the prior art, such as the lack of an adaptive adjustment model that analyzes muscle stiffness and fatigue through electromyography signals, integrates pressure and posture data to construct an adaptive adjustment model, and achieves linkage control of frequency, torque, and pressure.

[0008] In a first aspect, embodiments of this application provide a method for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun, applied to a fascia gun. The fascia gun includes a housing, a motor disposed within the housing, a massage head drive mechanism connected to the motor, a motor drive circuit, a control module, a pressure sensor disposed on the massage head drive mechanism or the massage head, an electromyography (EMG) signal sensor disposed on the housing or the massage head, and an acceleration sensor for acquiring the motion state of the fascia gun; the method includes:

[0009] The system receives pressure values ​​from the pressure sensor when the massage head contacts the human body, electromyographic signals from the electromyography sensor for the target muscle group, and fascia gun movement posture data from the accelerometer. Based on the electromyographic signals, the system analyzes the muscle hardness and muscle fatigue parameters of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient.

[0010] Based on the motor rotation frequency range, a target adjustment frequency is determined within a preset safe frequency range. A motor drive control signal is generated based on the output torque adjustment coefficient. The motor is controlled to run at the target adjustment frequency through the motor drive circuit, and the massage head drive mechanism is controlled so that the output pressure value of the massage head is linked and matched with the target adjustment frequency and the output torque adjustment coefficient.

[0011] If the detected contact pressure value exceeds the preset safety pressure threshold, the motor frequency is forcibly reduced, and the pressure adjustment component is controlled to reduce the output pressure of the massage head. If the electromyography signal sensor continuously collects electromyography signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a gradual adjustment of muscle relaxation intensity.

[0012] In some embodiments, the step of analyzing the muscle hardness parameters and muscle fatigue parameters of the target muscle group based on the electromyographic signal includes: performing noise reduction filtering on the electromyographic signal to extract time-domain and frequency-domain feature values ​​from the signal; determining the muscle hardness level of the target muscle group based on a preset muscle hardness-feature value mapping table, using the root mean square value, integral electromyographic value, and median frequency in the time-domain feature values; and classifying the fatigue level of the target muscle group based on the amplitude decay rate and frequency drift of the electromyographic signal, combined with preset fatigue judgment rules, wherein the fatigue level includes mild fatigue, moderate fatigue, and severe fatigue.

[0013] In some embodiments, determining the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range includes: performing an intersection operation between the motor rotation frequency range and the preset safe frequency range to obtain an effective frequency range; selecting a target adjustment frequency from the effective frequency range based on the midpoint value of the effective frequency range, user historical usage preference data, or the current muscle fatigue level, wherein when the muscle fatigue level is severe fatigue, the target adjustment frequency is preferentially selected from the lower limit value of the effective frequency range.

[0014] In some embodiments, generating a motor drive control signal based on the output torque adjustment coefficient includes: converting the output torque adjustment coefficient into a corresponding drive current correction parameter based on the rated torque-current characteristic curve of the motor; and adjusting the duty cycle or voltage amplitude of the reference drive signal by means of the drive current correction parameter in conjunction with the reference drive signal corresponding to the target adjustment frequency, thereby generating a motor drive control signal containing frequency control commands and torque compensation commands.

[0015] In some embodiments, controlling the motor to operate at the target adjustment frequency via the motor drive circuit includes: acquiring the motor speed feedback signal in real time using the motor's built-in speed sensor; comparing the speed feedback signal with the target adjustment frequency using a closed-loop control algorithm, and dynamically adjusting the drive parameters of the motor drive circuit to stabilize the motor speed within the error range of ±5% of the target adjustment frequency, wherein the closed-loop control algorithm includes a proportional-integral-derivative control algorithm.

[0016] In some embodiments, the massage head drive mechanism is provided with an electric push rod or an elastic pressure adjustment component; controlling the massage head drive mechanism to make the output pressure value of the massage head linked and matched with the target adjustment frequency and output torque adjustment coefficient includes: obtaining the target output pressure value corresponding to the target adjustment frequency and output torque adjustment coefficient according to a preset pressure-frequency-torque linkage mapping table; adjusting the output pressure value of the massage head to the target output pressure value by controlling the extension stroke of the electric push rod or the compression amount of the elastic pressure adjustment component, wherein the linkage mapping table is obtained by experimentally calibrating the optimal relaxation pressure under different muscle hardness levels.

[0017] In some embodiments, if the detected contact pressure value exceeds a preset safe pressure threshold, triggering a forced reduction mechanism for the motor frequency and controlling the pressure regulating component to reduce the output pressure of the massage head includes: when the contact pressure value exceeds 1.2 times the preset safe pressure threshold, triggering a first-level reduction, reducing the current operating frequency of the motor by 20%, and controlling the pressure regulating component to reduce the output pressure to 70% of the current value; if the contact pressure value continues to exceed the safe pressure threshold for 500 milliseconds, triggering a second-level reduction, further reducing the motor frequency by 30%, reducing the output pressure to 50% of the initial value, and issuing a pressure overload warning through the fascia gun's prompt module.

[0018] In some embodiments, if the electromyography (EMG) sensor continuously collects EMG signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is decreased to form a gradual adjustment of muscle relaxation intensity. This includes: setting the signal monitoring time window to 10-30 seconds; when the amplitude of the EMG signal is detected to show a step-like decrease for more than 3 consecutive cycles within the time window, it is determined that the muscle is in the relaxation adaptation stage; adjusting the motor rotation frequency according to the current muscle fatigue level and the preset frequency decrease gradient, and reducing the output pressure of the massage head by the same proportional coefficient, until the amplitude of the EMG signal tends to stabilize or reaches the preset minimum relaxation intensity threshold.

[0019] In some embodiments, obtaining the currently applicable motor rotation frequency range and corresponding output torque adjustment coefficient by combining contact pressure value and motion posture data includes: analyzing the three-dimensional posture angle of the fascia gun through the accelerometer to determine whether the current pressing direction of the massage head is perpendicular to the target muscle surface; when the tilt angle of the pressing direction exceeds a preset threshold, correcting and compensating the contact pressure value according to the tilt angle to obtain an equivalent vertical pressure value; based on the corrected equivalent vertical pressure value, muscle hardness parameters, and fatigue parameters, matching the corresponding motor frequency range and torque adjustment coefficient through a pre-trained adjustment rule library, wherein the adjustment rule library is generated by training muscle relaxation effect data under different pressing postures using a machine learning algorithm.

[0020] Secondly, this application provides a fascia gun adaptive frequency adjustment and muscle relaxation intensity control system, applied to a fascia gun. The fascia gun includes a housing, a motor disposed within the housing, a massage head drive mechanism connected to the motor, a motor drive circuit, a control module, a pressure sensor disposed on the massage head drive mechanism or the massage head, an electromyography signal sensor disposed on the housing or the massage head, and an acceleration sensor for acquiring the motion state of the fascia gun; including:

[0021] The coefficient acquisition unit is used to receive the pressure value of the massage head in contact with the human body collected by the pressure sensor, the electromyographic signal of the target muscle group collected by the electromyographic signal sensor, and the fascia gun movement posture data collected by the acceleration sensor; based on the electromyographic signal, it analyzes the muscle hardness parameters and muscle fatigue parameters of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient.

[0022] The linkage matching unit is used to determine the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range, and generate a motor drive control signal based on the output torque adjustment coefficient. The motor drive circuit controls the motor to run at the target adjustment frequency, and controls the massage head drive mechanism so that the output pressure value of the massage head is linked and matched with the target adjustment frequency and the output torque adjustment coefficient.

[0023] The intensity adjustment unit is used to trigger a forced reduction mechanism of motor frequency if the detected contact pressure value exceeds the preset safety pressure threshold, thereby controlling the pressure adjustment component to reduce the output pressure of the massage head; if the electromyography signal sensor continuously collects electromyography signals of the target muscle group showing a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a progressive muscle relaxation intensity adjustment.

[0024] This application provides a method and system for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun. By analyzing muscle stiffness and fatigue through electromyography (EMG) signals and combining this with pressure and posture data, a multi-dimensional input adjustment model is established. This allows the device to "sense" the real-time state of the muscles, changing the traditional, extensive adjustment method that relies on user experience. The adjustment precision is improved from the "gear level" to the "real-time dynamic matching level." Through preset mapping relationships and an adaptive model, the system achieves systematic linkage between motor frequency, output torque, and massage head pressure. For example, it automatically matches a high-frequency, high-intensity mode based on muscle stiffness and dynamically reduces stimulation intensity based on fatigue level, avoiding the limitations of single-parameter adjustment and significantly improving muscle relaxation efficiency and comfort. It not only has forced downshift protection in case of pressure overload but also judges the muscle adaptation state through periodic changes in EMG signals, achieving gradual intensity adjustment. This prevents tissue damage risks caused by excessive pressure and avoids overstimulation, forming a closed-loop control of "sensing-analysis-adjustment-protection," greatly improving the safety of device use. By analyzing the device's motion posture using an accelerometer, the pressure during tilted pressing is corrected and compensated, ensuring that the adjustment parameters in different usage scenarios are based on the actual effective pressure. This solves the problem of inaccurate control of traditional devices when pressing non-vertically, and improves the universality of the adjustment strategy.

[0025] In summary, this invention, through the fusion of intelligent algorithms and multiple sensors, constructs a complete technical system for adaptive control of fascia guns, breaking through the bottleneck of existing technologies that rely solely on manual adjustment or single parameter feedback, and achieving substantial technical improvements in accuracy, safety, and comfort.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart illustrating the steps of a fascia gun adaptive frequency adjustment and muscle relaxation intensity control method according to an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of the structure of a fascia gun provided in one embodiment of this application;

[0030] Figure 3 This is a schematic block diagram of a fascia gun adaptive frequency adjustment and muscle relaxation intensity control system provided in an embodiment of this application;

[0031] Figure 4 This is a schematic block diagram of the structure of a fascia gun provided in one embodiment of this application.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0035] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0036] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0037] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0039] Fascia guns, as common muscle relaxation devices, rely on a motor-driven high-frequency vibration of the massage head to relieve muscle tension. However, current fascia gun technology generally uses manual adjustment of preset levels for frequency and pressure control, which has the following significant drawbacks:

[0040] Lack of muscle state perception: Traditional solutions rely solely on the user's subjective judgment to select intensity levels, failing to obtain real-time physiological parameters such as the actual stiffness and fatigue level of the target muscle group, resulting in insufficient adjustment precision. For example, stiffer muscles require higher frequency stimulation, while fatigued muscles require gentle relaxation, but current technologies lack methods for quantitatively analyzing muscle state.

[0041] The current equipment lacks a systematic linkage between massage head pressure, motor frequency, and torque output. For example, when the pressing angle is tilted, the actual effective component of the contact pressure changes, but the equipment cannot automatically correct this, which may lead to pressure overload or reduced relaxation effect. Furthermore, pressure adjustment and frequency adjustment are independent of each other and have not formed a coordinated control logic based on muscle physiological characteristics.

[0042] The safety and comfort mechanisms are oversimplified: they only have basic overload protection (such as pressure switch power cut-off) and lack progressive adjustment strategies for the muscle relaxation process. For example, when the muscles adapt more during relaxation (electromyographic signals weaken), the intensity cannot be dynamically reduced to avoid overstimulation, or only a single down-level measure is taken when the pressure is abnormal, without graded protection based on the muscle fatigue state.

[0043] In the existing technology, there is no technical solution that uses electromyography signals to analyze muscle stiffness and fatigue, integrates pressure and posture data to build an adaptive adjustment model, and achieves coordinated control of frequency, torque, and pressure.

[0044] To resolve the above issues, please refer to... Figure 1 This application provides a method for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun, applicable to, for example... Figure 2 The fascia gun shown is described above. The fascia gun includes a housing, a motor housed within the housing, a massage head drive mechanism connected to the motor, a motor drive circuit, a control module, a pressure sensor located on the massage head drive mechanism or the massage head, an electromyography (EMG) sensor located on the housing or the massage head, and an acceleration sensor for collecting the fascia gun's motion status. It should also be noted that all information involved in the method provided in this application was extracted with the authorization of the relevant user and in accordance with relevant regulations, and will not infringe on user privacy.

[0045] The provided method for adaptive frequency adjustment and muscle relaxation intensity control of the fascia gun includes steps S101 to S103. Details are as follows:

[0046] Step S101. Receive the pressure value of the massage head in contact with the human body collected by the pressure sensor, the electromyographic signal of the target muscle group collected by the electromyographic signal sensor, and the fascia gun movement posture data collected by the accelerometer; Analyze the muscle hardness parameters and muscle fatigue parameters of the target muscle group based on the electromyographic signals, and combine the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient.

[0047] Specifically, this step involves real-time acquisition of three key data types using a multi-sensor system integrated into the fascia gun (pressure sensor, electromyography (EMG) sensor, and accelerometer): Pressure sensor data: acquiring the real-time pressure value FF of the massage head in contact with the body, reflecting the contact force of the massage head on the muscles; Electromyography (SEMG) data: acquiring EMG signals through electrode pads attached to the target muscle group, amplifying and filtering them to extract time-domain / frequency-domain features (such as root mean square value RMS and average power frequency MPF), used to analyze muscle stiffness (positively correlated with EMG signal amplitude) and fatigue (positively correlated with the decreasing trend of MPF); Accelerometer data: acquiring the three-dimensional acceleration (ax, ay, az) and attitude angles (pitch angle and roll angle) of the fascia gun, used to determine the pressing angle and movement posture of the massage head, and to calculate the effective component of the pressure (e.g., when pressing at an angle, the actual vertical pressure is F*cosθ, where θ is the pressing angle). Based on the above data, a muscle state-control parameter mapping relationship is established through a preset algorithm model (such as a neural network or fuzzy control rules): Muscle hardness parameter H: mapped after normalization of the RMS value of electromyography signal. The higher the hardness, the higher the required base frequency fbase; Muscle fatigue parameter D: calculated by MPF attenuation rate. The higher the fatigue, the lower the upper limit of frequency needs to be to avoid overstimulation; combined with the effective pressure component and posture data, the frequency adjustment range is corrected (such as increasing frequency compensation when the effective pressure is insufficient for high-hardness muscles), generating the motor rotation frequency range [fmin, fmax] and torque adjustment coefficient kT (used to dynamically adjust the motor torque output to compensate for the vibration effect attenuation caused by pressure changes).

[0048] Sensor Deployment: The pressure sensor is integrated at the connection between the massage head and the drive mechanism, using a strain gauge or piezoresistive sensor with an accuracy of ±1N; the electromyography (EMG) sensor uses dry or wet electrodes and is placed on the edge of the massage head or the grip area of ​​the housing to ensure good contact with the skin, with a built-in 20-500Hz bandpass filter and a 50Hz notch filter to remove power frequency interference; the accelerometer (6-axis or 9-axis IMU) is fixed at the center of the housing and outputs attitude quaternions or Euler angles in real time.

[0049] Signal processing flow: Electromyographic signal preprocessing: RMS and MPF are calculated through a sliding window (e.g., 500ms), and muscle state parameters are updated every 200ms; Pressure data correction: The vertical component of pressure F⊥=F*cosθ is calculated based on the posture angle, where θ is the angle between the massage head and the vertical direction.

[0050] Frequency range generation is achieved by establishing a lookup table or a regression model. Inputting H, D, and F⊥, the output is fmin = f0 * (1 + αH - βD), fmax = fmax_safe * (1 - γD) (where f0 is the reference frequency, and α, β, and γ are adjustment coefficients), and the torque coefficient kT = 1 + δ(F⊥ - Ftarget) (δ is the pressure compensation coefficient).

[0051] By quantitatively analyzing the muscle hardness and fatigue through electromyogram signals, replacing the traditional subjective gear selection, and achieving a closed-loop feedback of "muscle state → adjustment parameters". For example, muscles with high hardness are automatically matched with high-frequency stimulation (such as 3200 rpm), and the frequency upper limit of fatigued muscles is reduced (such as limited below 2000 rpm); combining the effective component of pressure and posture data to correct the adjustment parameters, avoiding misjudgment of pressure caused by the tilt of the pressing angle (such as automatically compensating the pressure to 70% of the original value when tilted at 45°), improving the control accuracy; the generated frequency range and torque coefficient provide the core input for subsequent dynamic adjustment, constructing a mapping link of "sensor data → physiological parameters → control parameters", breaking the isolated control of traditional independent adjustment.

[0052] Step S102. According to the motor rotation frequency range, determine the target adjustment frequency within the preset safe frequency range, and generate a motor drive control signal according to the output torque adjustment coefficient. Control the motor to operate at the target adjustment frequency through the motor drive circuit, and control the massage head drive mechanism to make the output pressure value of the massage head form a linkage match with the target adjustment frequency and the output torque adjustment coefficient.

[0053] Specifically, based on the frequency range [fmin, fmax] generated in step S101, combined with the preset safe frequency range [fsafe_min, fsafe_max] (such as 500 - 3500 rpm), the target adjustment frequency ftarget is determined through the following logic: if [fmin, fmax] is completely contained within the safe range, take the middle value or weight according to the fatigue degree (such as偏向 fmin when the fatigue degree is high); if it exceeds the safe range, automatically clamp to the boundary (such as taking fsafe_min when fmin < fsafe_min). Generate the motor drive signal according to the torque adjustment coefficient kT: the motor drive circuit (such as H-bridge drive) adjusts the PWM duty cycle according to ftarget and kT, where kT is used to compensate for the influence of pressure change on the motor load (such as increasing the torque when the pressure increases to avoid speed fluctuation); at the same time,联动调整 the output pressure of the massage head through a pressure adjustment component (such as a spring-damper mechanism or a servo motor) to make Foutput match ftarget and kT (such as reducing the pressure at high frequencies to avoid overload and increasing the pressure at low frequencies to enhance penetration).

[0054] Target frequency calculation: Safety interval verification: ftarget=clip((fmin+fmax) / 2,fsafe_min,fsafe_max), where the clip function is a boundary clamp; Fatigue weighting: If the fatigue degree D>Dthreshold, then ftarget=fmin+(fmax−fmin)*(1−D / Dmax), to achieve gentle relaxation of fatigued muscles.

[0055] Linkage control implementation: Motor drive: A closed-loop vector control algorithm is adopted. The speed loop target is set according to ftarget, and the current loop limit is adjusted by kT (torque is positively correlated with current).

[0056] Pressure linkage: Establish a reverse compensation model of Foutput=Fbase+η(ftarget−fbase) (η is a negative coefficient, and the base pressure Fbase is automatically reduced at high frequencies), and achieve dynamic pressure adjustment by adjusting the spring compression through a micro servo motor or solenoid valve.

[0057] Breaking away from the traditional approach of independently adjusting frequency and pressure, it achieves synergistic optimization of "frequency-torque-pressure" (e.g., high frequency and low pressure avoid muscle impact, while low frequency and high pressure enhance deep relaxation), resolving the issue of effective pressure deviation caused by changes in the pressing angle; it prevents motor overspeed or overload through safety range clamping, and dynamically selects the optimal frequency based on muscle condition (e.g., using the upper limit of high frequency for hard muscles and the lower limit of low frequency for fatigued muscles), improving relaxation efficiency; the torque adjustment coefficient compensates in real time for the impact of pressure changes on the motor load, ensuring speed stability (e.g., automatically increasing torque when pressure suddenly increases to avoid a sudden drop in speed that leads to a decrease in relaxation effect).

[0058] Step S103. If the contact pressure value is detected to exceed the preset safe pressure threshold, the motor frequency forced downshift mechanism is triggered, and the pressure adjustment component is controlled to reduce the output pressure of the massage head; if the electromyography signal sensor continuously collects electromyography signals of the target muscle group showing a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a progressive muscle relaxation intensity adjustment.

[0059] Specifically, this step includes two types of dynamic adjustment mechanisms: Overload safety protection: When the detected contact pressure value F⊥>Fsafe_threshold (such as 50N, which can be preset according to the user's body type), trigger the forced downshift mechanism: immediately reduce the motor frequency by 1-2 gears (such as from 3000rpm to 2500rpm), and at the same time control the pressure adjustment component to reduce the output pressure of the massage head (such as releasing pressure through spring rebound) to avoid impact damage to muscles or bones; Progressive relaxation adjustment: When the electromyogram signal shows periodic weakening (RMS value drops >10%) for 3 consecutive cycles (such as 600ms), it indicates that the muscle adaptability increases and the fatigue degree decreases. At this time, dynamically reduce the stimulation intensity: gradually reduce the frequency (reduce 100rpm each time, with an interval of 10s) and output pressure (reduce 5N each time) to form a progressive relaxation process of "high-intensity start → adaptive attenuation" to avoid muscle rebound tension caused by overstimulation.

[0060] Overload detection and response: Pressure threshold grading: Set the first-level threshold (warning, such as 40N) to trigger a beeping reminder, and the second-level threshold (forced downshift, such as 50N) to trigger a control action; Downshift strategy: Adopt "frequency first downshift + pressure linkage attenuation", that is, when the frequency is downshifted, the pressure adjustment component releases the spring pressure by rotating the motor backward 0.5 turns, so that Foutput quickly drops by 10-15N.

[0061] Progressive adjustment logic: Electromyogram trend detection: Compare the RMS values of 3 consecutive cycles through a sliding window. If RMSt<RMSt−1×0.9 and it occurs 3 times continuously, it is determined as "enhanced adaptability"; Adjustment rate: The adjustment amplitude of the frequency is 5% of the current frequency each time (not exceeding 150rpm), the pressure is adjusted by 5N each time, and the adjustment interval is 30s to avoid over-frequent adjustment affecting the experience.

[0062] Different from traditional single power-off protection, through hierarchical pressure thresholds and linked downshifts, quickly reduce the stimulation intensity during overload while maintaining controllable operation, avoiding user discomfort caused by sudden shutdown;

[0063] Dynamically attenuate the intensity according to the muscle relaxation process, in line with the physiological adaptation law (such as automatically reducing the frequency after muscle relaxation to avoid "over-massage after relaxation"), and reduce secondary fatigue caused by constant intensity;

[0064] Combine the dynamic adjustment of the protection strategy with the muscle fatigue degree (such as setting the pressure threshold of fatigued muscles to 40N and healthy muscles to 50N) to achieve personalized safety control and solve the problem of the crude "one-size-fits-all" downshift of traditional methods.

[0065] Steps S101-S103 form a closed-loop system of "data acquisition → status analysis → linkage control → safety optimization" through three steps. The core breakthroughs are: physiological parameter-driven regulation: quantifying muscle state based on electromyographic signals to replace subjective judgment and achieve "precise perception-intelligent decision-making"; multi-parameter coupled control: integrating pressure, posture, and electromyographic data to construct a regulation model, solving the lack of synergy in traditional independent regulation; dynamic adaptation and safety: balancing relaxation effects with user safety through progressive strategies and graded protection, conforming to ergonomics and physiological principles. This solution fundamentally addresses the core deficiencies of existing fascia guns, providing a reusable engineering paradigm for the adaptive control of intelligent rehabilitation equipment.

[0066] In some embodiments, the step of analyzing the muscle hardness parameters and muscle fatigue parameters of the target muscle group based on the electromyographic signal includes: performing noise reduction filtering on the electromyographic signal to extract time-domain and frequency-domain feature values ​​from the signal; determining the muscle hardness level of the target muscle group based on a preset muscle hardness-feature value mapping table, using the root mean square value, integral electromyographic value, and median frequency in the time-domain feature values; and classifying the fatigue level of the target muscle group based on the amplitude decay rate and frequency drift of the electromyographic signal, combined with preset fatigue judgment rules, wherein the fatigue level includes mild fatigue, moderate fatigue, and severe fatigue.

[0067] By preprocessing and feature analysis of electromyography (sEMG) signals, muscle stiffness and fatigue are quantified: Signal preprocessing: Noise reduction filtering is performed on the raw EMG signals (e.g., 50Hz notch filtering to remove power frequency interference, bandpass filtering to extract the effective frequency band of 20-500Hz), eliminating motion artifacts and environmental noise; Feature extraction: Time domain features: Root mean square (RMS) and integral electromyography (IEMG) values ​​are calculated to reflect the degree of muscle activation (higher values ​​indicate higher muscle stiffness); Frequency domain features: Median frequency (MDF) is calculated using Fast Fourier Transform (FFT) to reflect... Muscle fatigue status (decreased MDF indicates increased fatigue); Hardness grading: Based on the matching relationship between RMS, IEMG, MDF and a preset mapping table, muscle hardness is divided into 3 levels (e.g., low hardness: RMS < 150 μV, medium hardness: 150-300 μV, high hardness: > 300 μV); Fatigue grading: The fatigue level is determined by the amplitude decay rate (RMS decrease rate) and frequency drift degree (MDF decrease rate), combined with a rule base (mild fatigue: MDF decrease < 10%, moderate: 10%-20%, severe: > 20%).

[0068] Signal processing flow: A second-order Butterworth bandpass filter (20-500Hz) and a 50Hz notch filter are used to update the feature values ​​every 100ms; the mapping table is calibrated through clinical trials, and sEMG data of muscles with different stiffness (such as relaxed, tense, and spastic states) are collected to establish the correspondence between feature values ​​and stiffness levels; fatigue judgment rule: if the MDF decreases by more than 20% and the RMS decreases by more than 15% within 10s, it is judged as severe fatigue, triggering the low-frequency protection mechanism.

[0069] The electromyography sensor integrates a 24-bit ADC with a sampling rate of 1000Hz, and uses Ag / AgCl dry electrodes to reduce skin irritation; the embedded MCU (such as STM32) runs digital signal processing algorithms in real time and reduces CPU load by transferring data through DMA.

[0070] By replacing single subjective judgment with multi-feature fusion (time domain + frequency domain), the hardness grading error is ≤10% and the fatigue recognition accuracy is ≥90%. It provides physiological parameter basis for subsequent frequency and pressure adjustment (such as automatically matching high frequency range for high-hardness muscles and forcibly restricting low frequency for severe fatigue), solving the problem of traditional "blind adjustment". The mapping table and judgment rules can be upgraded via OTA to adapt to the differences in muscle characteristics of different user groups (such as athletes and sedentary people).

[0071] In some embodiments, determining the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range includes: performing an intersection operation between the motor rotation frequency range and the preset safe frequency range to obtain an effective frequency range; selecting a target adjustment frequency from the effective frequency range based on the midpoint value of the effective frequency range, user historical usage preference data, or the current muscle fatigue level, wherein when the muscle fatigue level is severe fatigue, the target adjustment frequency is preferentially selected from the lower limit value of the effective frequency range.

[0072] Dynamically determine the optimal frequency under safety constraints: Effective frequency range calculation: Find the intersection of the frequency range generated in step S101 with the preset safety range (e.g., 500-3500rpm) to ensure that the adjusted frequency is within the safe operating range of the motor; Frequency selection strategy: Midpoint value priority: When there is no significant fatigue, take the midpoint value of the effective range (e.g., take 2500rpm for the range [2000-3000rpm]); User preference fusion: Combine historical usage data (e.g., the user's commonly used 2800rpm) and adjust the target frequency by weighted average; Fatigue degree guidance: When there is severe fatigue, forcibly select the lower limit of the range (e.g., take 1500rpm for the effective range [1500-2500rpm]) to avoid high-frequency stimulation aggravating muscle damage.

[0073] Intersection operation logic: Effective lower limit feff_min = max(fmin, fsafe_min), effective upper limit feff_max = min(fmax, fsafe_max); if feff_min > feff_max, then the midpoint value of the safe interval (fsafe_min + fsafe_max) / 2 is taken as the default value. Preference data processing: Locally store the frequency data of the user's most recent 10 uses, calculate the average frequency fler, with a weight of 30% (e.g., ftarget = 0.7 × fmid + 0.3 × fler); Fatigue weight: mild fatigue weight 0.2, moderate fatigue 0.5, severe fatigue 1.0 (directly take the lower limit value).

[0074] Intersection operations are used to avoid motor overspeed or low-frequency stalling, ensuring long-term reliable operation of the equipment; user habits and physiological states (such as forced frequency reduction when fatigued) are integrated to balance versatility and customized needs; a low-frequency strategy is prioritized when severely fatigued to reduce the risk of overstimulation due to insufficient muscle endurance, which is in line with the principles of sports rehabilitation medicine.

[0075] In some embodiments, generating a motor drive control signal based on the output torque adjustment coefficient includes: converting the output torque adjustment coefficient into a corresponding drive current correction parameter based on the rated torque-current characteristic curve of the motor; and adjusting the duty cycle or voltage amplitude of the reference drive signal by means of the drive current correction parameter in conjunction with the reference drive signal corresponding to the target adjustment frequency, thereby generating a motor drive control signal containing frequency control commands and torque compensation commands.

[0076] The torque adjustment coefficient is converted into motor drive parameters to achieve dynamic load compensation: Torque-current mapping: Based on the rated torque-current characteristic curve of the motor calibrated at the factory (such as torque T ∝ drive current I), the torque adjustment coefficient kT generated in step S101 is converted into the current correction parameter ΔI=I0×(kT−1) (I0 is the reference current); Drive signal modulation: Based on the reference PWM signal corresponding to the target frequency, the torque compensation command is superimposed by adjusting the duty cycle or voltage amplitude (such as Buck circuit voltage regulation) to ensure that the motor maintains stable speed when the pressure changes.

[0077] Characteristic curve calibration: Under laboratory conditions, at a fixed frequency of 3000 rpm, the load pressure is gradually increased (10-50N), and the relationship between motor current and torque is recorded to generate a linear fitting equation of T=kI×I+b; the torque coefficient kT=F⊥ / Fnominal (F⊥ is the corrected vertical pressure, Fnominal is the nominal pressure of 20N). When F⊥=30N, kT=1.5, corresponding to a 50% increase in current.

[0078] Drive circuit control: An H-bridge drive circuit is adopted, and the STM32 outputs two complementary PWM signals with a duty cycle D=D0+ΔD, where ΔD is determined by kT (e.g., when kT=1.2, ΔD=+10%). The built-in current sensor monitors the drive current in real time and prevents motor overload through an overcurrent protection mechanism.

[0079] When the pressing pressure changes (e.g., from 20N to 30N), the drive current is automatically increased to compensate for torque attenuation, ensuring speed fluctuations are ≤5% and maintaining a stable vibration effect; current over-limit is avoided through characteristic curve mapping, extending motor life and preventing sudden speed drops due to sudden pressure increases (a common problem in traditional solutions); torque and pressure are linked in real time, solving the inefficiency problem of "the harder you press, the weaker the vibration", and ensuring consistent relaxation intensity under different pressures.

[0080] In some embodiments, controlling the motor to operate at the target adjustment frequency via the motor drive circuit includes: acquiring the motor speed feedback signal in real time using the motor's built-in speed sensor; comparing the speed feedback signal with the target adjustment frequency using a closed-loop control algorithm, and dynamically adjusting the drive parameters of the motor drive circuit to stabilize the motor speed within the error range of ±5% of the target adjustment frequency, wherein the closed-loop control algorithm includes a proportional-integral-derivative control algorithm.

[0081] High-precision frequency control is achieved through speed feedback: Speed ​​acquisition: The speed pulse signal is acquired in real time using the motor's built-in Hall sensor or encoder, and the actual speed nreal is calculated; Closed-loop regulation: The PID (proportional-integral-derivative) control algorithm is adopted to compare nreal with the target frequency ftarget and dynamically adjust the PWM duty cycle to keep the speed stable within the range of ftarget ±5%.

[0082] Sensor configuration: The brushless motor integrates 3 Hall sensors, outputting 6 pulses per revolution. The MCU captures the pulse period through a timer to calculate the rotational speed (accuracy ±10rpm); the sampling period is set to 50ms to ensure real-time performance.

[0083] PID parameter tuning: proportional coefficient Kp=0.1, integral coefficient Ki=0.05, derivative coefficient Kd=0.02 (optimized by Ziegler-Nichols method); control logic: ΔD=Kp×e+Ki×∑e+Kd×(e−eprev), where e=ftarget−nreal.

[0084] With a rotational speed error of ≤5%, significantly better than traditional open-loop control (error typically >10%), ensuring frequency stability under different loads; when load fluctuations occur due to changes in pressing angle or muscle hardness, the closed-loop system responds quickly (adjustment time <200ms), avoiding rotational speed drift from affecting the relaxation effect; it avoids current surges caused by sudden load changes, reduces mechanical wear through smooth adjustment, and improves equipment durability.

[0085] In some embodiments, the massage head drive mechanism is provided with an electric push rod or an elastic pressure adjustment component; controlling the massage head drive mechanism to make the output pressure value of the massage head linked and matched with the target adjustment frequency and output torque adjustment coefficient includes: obtaining the target output pressure value corresponding to the target adjustment frequency and output torque adjustment coefficient according to a preset pressure-frequency-torque linkage mapping table; adjusting the output pressure value of the massage head to the target output pressure value by controlling the extension stroke of the electric push rod or the compression amount of the elastic pressure adjustment component, wherein the linkage mapping table is obtained by experimentally calibrating the optimal relaxation pressure under different muscle hardness levels.

[0086] The coordinated adjustment of pressure, frequency, and torque is achieved through electric push rods or elastic mechanisms: Linkage mapping table: The optimal relaxation pressure for different muscle hardness levels is calibrated through experiments (e.g., 30-40N for high-hardness muscles and 15-25N for low-hardness muscles), establishing a three-dimensional mapping relationship of "frequency-torque-pressure"; Pressure control execution: Based on the target frequency and torque coefficient, the target pressure value is retrieved from the mapping table, driving the electric push rod to adjust the massage head stroke or compress the elastic component (such as a spring), achieving dynamic matching of output pressure.

[0087] Mapping table establishment: Recruit users with different physical conditions and test the relationship between different pressures (10-50N) and the attenuation rate of electromyographic signals under high / medium / low muscle stiffness conditions to determine the optimal pressure range; the table structure is shown in the example below:

[0088] Frequency (rpm) Torque coefficient Hardness rating Target pressure (N) 2500 1.0 middle 25-30 3000 1.2 high 30-35

[0089] Hardware implementation: Electric linear actuator solution: A miniature servo linear actuator (10mm stroke, 0.1mm accuracy) is used, and the stroke is converted into pressure through a lead screw and nut mechanism (e.g., 5N pressure for every 1mm stroke); Elastic component solution: Adjustable spring compression, the position of the nut is adjusted by rotating a stepper motor to change the spring preload (e.g., the pressure increases by 10N for every 2mm increase in compression).

[0090] It solves the problem of the separation of traditional "independent frequency and pressure adjustment", such as automatically reducing pressure at high frequencies (e.g., 3000rpm corresponds to 30N, 2000rpm corresponds to 40N) to avoid muscle damage caused by high frequency and high pressure; the pressure adjustment accuracy is ±2N, and the mapping table matches muscle hardness to achieve targeted relaxation of "high pressure for hard muscles and low pressure for soft muscles", improving efficiency by more than 30%; the experimentally calibrated mapping table provides a quantitative benchmark for the control logic, reducing the complexity of algorithm development and ensuring consistency between different batches of equipment.

[0091] In some embodiments, if the detected contact pressure value exceeds a preset safe pressure threshold, triggering a forced reduction mechanism for the motor frequency and controlling the pressure regulating component to reduce the output pressure of the massage head includes: when the contact pressure value exceeds 1.2 times the preset safe pressure threshold, triggering a first-level reduction, reducing the current operating frequency of the motor by 20%, and controlling the pressure regulating component to reduce the output pressure to 70% of the current value; if the contact pressure value continues to exceed the safe pressure threshold for 500 milliseconds, triggering a second-level reduction, further reducing the motor frequency by 30%, reducing the output pressure to 50% of the initial value, and issuing a pressure overload warning through the fascia gun's prompt module.

[0092] A two-level downshifting strategy is designed to address pressure exceeding limits, balancing safety and controllability. Threshold levels are categorized as follows: Level 1 Threshold (Warning): 1.2 times the safe pressure threshold Fsafe (e.g., Fsafe = 40N, Level 1 threshold 48N), triggering frequency downshifting and pressure attenuation; Level 2 Threshold (Forced Protection): If the pressure exceeds the Level 1 threshold for 500ms, further downshifting and a warning are issued; Control Actions: Level 1 Downshifting: Frequency reduced by 20% (e.g., 3000rpm → 2400rpm), pressure reduced to 70% of the current value (e.g., 48N → 33.6N); Level 2 Downshifting: Frequency further reduced by 30% (2400rpm → 1680rpm), pressure reduced to 50% of the initial value (e.g., initial 40N → 20N), and the user is alerted via LED lights / buzzer.

[0093] Threshold setting and detection: The safe pressure threshold is adaptive based on the user's body size (e.g., 40N for adults, 30N for teenagers), calculated using the user's weight and height data input during device initialization; the pressure sensor has a real-time sampling rate of 100Hz, using a sliding window (5 sampling points) to determine if the limit is exceeded, avoiding false triggering. Actuator control: When downshifting, the motor PWM duty cycle (frequency control) is adjusted first, and commands are simultaneously sent to the pressure regulating component (e.g., the electric actuator moves 5mm in the opposite direction to release pressure); the warning signal is synchronized to the mobile APP via Bluetooth, displaying the cause of pressure overload and the suggested pressing angle.

[0094] Unlike traditional "one-size-fits-all" power-off protection, the first-level downshift retains basic relaxation functions, while the second-level downshift enforces safety protection to prevent user discomfort caused by sudden shutdown; continuous pressure exceeding the limit triggers stricter protection to effectively prevent bone damage (such as when pressing on bony prominences), meeting ergonomic safety standards; and provides audible and visual warnings to indicate operational problems (such as tilted pressing angle) to guide users to adjust their posture, improving safety and standardization of use.

[0095] In some embodiments, if the electromyography (EMG) sensor continuously collects EMG signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is decreased to form a gradual adjustment of muscle relaxation intensity. This includes: setting the signal monitoring time window to 10-30 seconds; when the amplitude of the EMG signal is detected to show a step-like decrease for more than 3 consecutive cycles within the time window, it is determined that the muscle is in the relaxation adaptation stage; adjusting the motor rotation frequency according to the current muscle fatigue level and the preset frequency decrease gradient, and reducing the output pressure of the massage head by the same proportional coefficient, until the amplitude of the EMG signal tends to stabilize or reaches the preset minimum relaxation intensity threshold.

[0096] Dynamic attenuation of stimulation intensity based on electromyographic signal trend: Relaxation phase identification: Set a monitoring window of 10-30 seconds. If the amplitude of the electromyographic signal (RMS) decreases by ≥10% for 3 consecutive cycles (2 seconds per cycle), the muscle is determined to have entered the adaptation phase; Gradient adjustment strategy: Set a frequency reduction gradient according to the fatigue level (50 rpm for mild fatigue, 100 rpm for moderate fatigue, and 150 rpm for severe fatigue), and simultaneously reduce the pressure proportionally (e.g., 10% reduction in frequency, 10% reduction in pressure) until the signal stabilizes or reaches the minimum intensity (e.g., 1500 rpm, 20 N).

[0097] Trend detection algorithm: The sliding window length is set to 20 seconds, and the RMS value is extracted every 2 seconds to form a sequence {RMS1, RMS2, ..., RMS10}; Judgment condition: There are 3 consecutive RMSi>RMSi+1×1.1, and the average decrease rate is >15%. Adjustment logic implementation: The adjustment interval is set to 30 seconds to avoid excessively frequent adjustments affecting the experience; Minimum intensity threshold: frequency 1500rpm, pressure 15N (to prevent the relaxation effect from being lost due to excessively low intensity); The intensity change curve is displayed in real time during the adjustment process (via the device's OLED screen or APP) to enhance user perception.

[0098] The intensity decreases dynamically according to the degree of muscle relaxation, conforming to the physiological process of "activation → relaxation → recovery," avoiding secondary muscle tension caused by "over-massage after relaxation." Gradual adjustment rather than abrupt downsizing reduces the user's sensory impact, making it particularly suitable for gentle relaxation of fatigued muscles (such as post-exercise recovery scenarios). The timing of adjustment is objectively determined by electromyographic signal trends, replacing the blindness of traditional fixed-time downsizing and improving the scientific rigor of the relaxation program. Example 8: Pressure Posture Compensation and Rule Base Matching

[0099] In some embodiments, obtaining the currently applicable motor rotation frequency range and corresponding output torque adjustment coefficient by combining contact pressure value and motion posture data includes: analyzing the three-dimensional posture angle of the fascia gun through the accelerometer to determine whether the current pressing direction of the massage head is perpendicular to the target muscle surface; when the tilt angle of the pressing direction exceeds a preset threshold, correcting and compensating the contact pressure value according to the tilt angle to obtain an equivalent vertical pressure value; based on the corrected equivalent vertical pressure value, muscle hardness parameters, and fatigue parameters, matching the corresponding motor frequency range and torque adjustment coefficient through a pre-trained adjustment rule library, wherein the adjustment rule library is generated by training muscle relaxation effect data under different pressing postures using a machine learning algorithm.

[0100] Combining posture data to correct pressure deviation and optimize adjustment parameters: Posture analysis: Calculate the massage head pressing angle (angle θ with the vertical direction) using an accelerometer to determine if the tilt exceeds a preset threshold (e.g., 15°); Pressure correction: When tilted, the actual vertical pressure F⊥=F×cosθ. If θ>15°, compensate proportionally to cosθ (e.g., when θ=30°, F⊥=0.866F); Rule base matching: Utilize a pre-trained adjustment rule base (trained based on muscle relaxation effect data under different postures), input the corrected pressure, hardness, and fatigue level, and output the matching frequency range and torque coefficient.

[0101] Attitude calculation method: complementary filtering is used to fuse accelerometer and gyroscope data to calculate pitch and roll angles in real time with an accuracy of ±1°; the tilt threshold is set to 15° (clinical verification shows that the effective pressure component decreases significantly when the angle exceeds this).

[0102] Rule base training: Data collection: Record the optimal frequency / torque combination of muscles with different stiffness at compression angles of 0°, 15°, 30°, and 45° (using the electromyographic signal decay rate as the evaluation index); Model construction: Use decision trees or fuzzy logic algorithms to establish the mapping rule of θ→[fmin,fmax,kT]. For example, when θ=30°, the upper limit of frequency is increased by 5% to compensate for pressure loss.

[0103] It solves the problem of pressure misjudgment caused by the tilt of the pressing angle (traditional solutions directly use the original pressure value, with an error of more than 30%), ensuring that the control parameters are based on the actual effective pressure; the rule base trained by machine learning adapts to a variety of usage scenarios (such as the natural tilt when the user operates with one hand), eliminating the need for manual posture calibration and improving the ease of use of the device; no matter how the pressing angle changes, the system automatically compensates for the pressure component to maintain a stable muscle stimulation intensity and avoids the effect attenuation caused by improper operation posture.

[0104] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of the fascia gun adaptive frequency adjustment and muscle relaxation intensity control system 200 provided in this application embodiment. The fascia gun adaptive frequency adjustment and muscle relaxation intensity control system 200 is used to execute the steps of the fascia gun adaptive frequency adjustment and muscle relaxation intensity control methods shown in the above embodiments. The fascia gun adaptive frequency adjustment and muscle relaxation intensity control system 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0105] like Figure 3 As shown, the fascia gun adaptive frequency adjustment and muscle relaxation intensity control system 200 includes:

[0106] The coefficient acquisition unit 201 is used to receive the pressure value of the massage head in contact with the human body collected by the pressure sensor, the electromyographic signal of the target muscle group collected by the electromyographic signal sensor, and the fascia gun movement posture data collected by the acceleration sensor; based on the electromyographic signal, it analyzes the muscle hardness parameter and muscle fatigue parameter of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient.

[0107] The linkage matching unit 202 is used to determine the target adjustment frequency within a preset safe frequency range according to the motor rotation frequency range, and generate a motor drive control signal according to the output torque adjustment coefficient. It controls the motor to run at the target adjustment frequency through the motor drive circuit, and controls the massage head drive mechanism so that the output pressure value of the massage head is linked and matched with the target adjustment frequency and the output torque adjustment coefficient.

[0108] The intensity adjustment unit 203 is used to trigger a forced reduction mechanism of motor frequency if the detected contact pressure value exceeds the preset safety pressure threshold, and control the pressure adjustment component to reduce the output pressure of the massage head; if the electromyography signal sensor continuously collects electromyography signals of the target muscle group showing a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a progressive muscle relaxation intensity adjustment.

[0109] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the fascia gun adaptive frequency adjustment and muscle relaxation intensity control system and each module described above can be referred to the corresponding content in the various embodiments of the fascia gun adaptive frequency adjustment and muscle relaxation intensity control method, and will not be repeated here.

[0110] The aforementioned method for adaptive frequency adjustment and muscle relaxation intensity control using a fascia gun can be implemented as a computer program, which can be used in various ways, such as... Figure 3 It runs on the device shown.

[0111] Please see Figure 4 , Figure 4 This is a schematic block diagram of the fascia gun provided in an embodiment of this application. The fascia gun includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0112] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any method of adaptive frequency adjustment and muscle relaxation intensity control for the fascia gun.

[0113] The processor provides computing and control capabilities to support the operation of the entire fascia gun.

[0114] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method of adaptive frequency adjustment and muscle relaxation intensity control for the fascia gun.

[0115] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. A specific fascia gun may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0117] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0118] The system receives pressure values ​​from the pressure sensor when the massage head contacts the human body, electromyographic signals from the electromyography sensor for the target muscle group, and fascia gun movement posture data from the accelerometer. Based on the electromyographic signals, the system analyzes the muscle hardness and muscle fatigue parameters of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient.

[0119] Based on the motor rotation frequency range, a target adjustment frequency is determined within a preset safe frequency range. A motor drive control signal is generated based on the output torque adjustment coefficient. The motor is controlled to run at the target adjustment frequency through the motor drive circuit, and the massage head drive mechanism is controlled so that the output pressure value of the massage head is linked and matched with the target adjustment frequency and the output torque adjustment coefficient.

[0120] If the detected contact pressure value exceeds the preset safety pressure threshold, the motor frequency is forcibly reduced, and the pressure adjustment component is controlled to reduce the output pressure of the massage head. If the electromyography signal sensor continuously collects electromyography signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a gradual adjustment of muscle relaxation intensity.

[0121] In some embodiments, the step of analyzing the muscle hardness parameters and muscle fatigue parameters of the target muscle group based on the electromyographic signal includes: performing noise reduction filtering on the electromyographic signal to extract time-domain and frequency-domain feature values ​​from the signal; determining the muscle hardness level of the target muscle group based on a preset muscle hardness-feature value mapping table, using the root mean square value, integral electromyographic value, and median frequency in the time-domain feature values; and classifying the fatigue level of the target muscle group based on the amplitude decay rate and frequency drift of the electromyographic signal, combined with preset fatigue judgment rules, wherein the fatigue level includes mild fatigue, moderate fatigue, and severe fatigue.

[0122] In some embodiments, determining the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range includes: performing an intersection operation between the motor rotation frequency range and the preset safe frequency range to obtain an effective frequency range; selecting a target adjustment frequency from the effective frequency range based on the midpoint value of the effective frequency range, user historical usage preference data, or the current muscle fatigue level, wherein when the muscle fatigue level is severe fatigue, the target adjustment frequency is preferentially selected from the lower limit value of the effective frequency range.

[0123] In some embodiments, generating a motor drive control signal based on the output torque adjustment coefficient includes: converting the output torque adjustment coefficient into a corresponding drive current correction parameter based on the rated torque-current characteristic curve of the motor; and adjusting the duty cycle or voltage amplitude of the reference drive signal by means of the drive current correction parameter in conjunction with the reference drive signal corresponding to the target adjustment frequency, thereby generating a motor drive control signal containing frequency control commands and torque compensation commands.

[0124] In some embodiments, controlling the motor to operate at the target adjustment frequency via the motor drive circuit includes: acquiring the motor speed feedback signal in real time using the motor's built-in speed sensor; comparing the speed feedback signal with the target adjustment frequency using a closed-loop control algorithm, and dynamically adjusting the drive parameters of the motor drive circuit to stabilize the motor speed within the error range of ±5% of the target adjustment frequency, wherein the closed-loop control algorithm includes a proportional-integral-derivative control algorithm.

[0125] In some embodiments, the massage head drive mechanism is provided with an electric push rod or an elastic pressure adjustment component; controlling the massage head drive mechanism to make the output pressure value of the massage head linked and matched with the target adjustment frequency and output torque adjustment coefficient includes: obtaining the target output pressure value corresponding to the target adjustment frequency and output torque adjustment coefficient according to a preset pressure-frequency-torque linkage mapping table; adjusting the output pressure value of the massage head to the target output pressure value by controlling the extension stroke of the electric push rod or the compression amount of the elastic pressure adjustment component, wherein the linkage mapping table is obtained by experimentally calibrating the optimal relaxation pressure under different muscle hardness levels.

[0126] In some embodiments, if the detected contact pressure value exceeds a preset safe pressure threshold, triggering a forced reduction mechanism for the motor frequency and controlling the pressure regulating component to reduce the output pressure of the massage head includes: when the contact pressure value exceeds 1.2 times the preset safe pressure threshold, triggering a first-level reduction, reducing the current operating frequency of the motor by 20%, and controlling the pressure regulating component to reduce the output pressure to 70% of the current value; if the contact pressure value continues to exceed the safe pressure threshold for 500 milliseconds, triggering a second-level reduction, further reducing the motor frequency by 30%, reducing the output pressure to 50% of the initial value, and issuing a pressure overload warning through the fascia gun's prompt module.

[0127] In some embodiments, if the electromyography (EMG) sensor continuously collects EMG signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is decreased to form a gradual adjustment of muscle relaxation intensity. This includes: setting the signal monitoring time window to 10-30 seconds; when the amplitude of the EMG signal is detected to show a step-like decrease for more than 3 consecutive cycles within the time window, it is determined that the muscle is in the relaxation adaptation stage; adjusting the motor rotation frequency according to the current muscle fatigue level and the preset frequency decrease gradient, and reducing the output pressure of the massage head by the same proportional coefficient, until the amplitude of the EMG signal tends to stabilize or reaches the preset minimum relaxation intensity threshold.

[0128] In some embodiments, obtaining the currently applicable motor rotation frequency range and corresponding output torque adjustment coefficient by combining contact pressure value and motion posture data includes: analyzing the three-dimensional posture angle of the fascia gun through the accelerometer to determine whether the current pressing direction of the massage head is perpendicular to the target muscle surface; when the tilt angle of the pressing direction exceeds a preset threshold, correcting and compensating the contact pressure value according to the tilt angle to obtain an equivalent vertical pressure value; based on the corrected equivalent vertical pressure value, muscle hardness parameters, and fatigue parameters, matching the corresponding motor frequency range and torque adjustment coefficient through a pre-trained adjustment rule library, wherein the adjustment rule library is generated by training muscle relaxation effect data under different pressing postures using a machine learning algorithm.

[0129] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the fascia gun adaptive frequency adjustment and muscle relaxation intensity control method provided in any embodiment of this application.

[0130] The computer-readable storage medium can be the internal storage unit of the fascia gun described in the foregoing embodiments, such as the hard drive or memory of the fascia gun. Alternatively, the computer-readable storage medium can be an external storage device of the fascia gun, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the fascia gun.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for adaptive frequency adjustment and muscle relaxation intensity control of a fascia gun, characterized in that, The method is applied to a fascia gun, which includes a housing, a motor housed within the housing, a massage head drive mechanism connected to the motor, a motor drive circuit, a control module, a pressure sensor located on the massage head drive mechanism or the massage head, an electromyography (EMG) sensor located on the housing or the massage head, and an acceleration sensor for acquiring the motion state of the fascia gun; the method includes: The system receives pressure values ​​from the pressure sensor when the massage head contacts the human body, electromyographic signals from the electromyography sensor for the target muscle group, and fascia gun movement posture data from the accelerometer. Based on the electromyographic signals, the system analyzes the muscle hardness and muscle fatigue parameters of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient. Based on the motor rotation frequency range, a target adjustment frequency is determined within a preset safe frequency range, and a motor drive control signal is generated based on the output torque adjustment coefficient. This includes: converting the output torque adjustment coefficient into a corresponding drive current correction parameter based on the motor's rated torque-current characteristic curve; adjusting the duty cycle or voltage amplitude of the reference drive signal according to the drive current correction parameter, in conjunction with the reference drive signal corresponding to the target adjustment frequency, to generate a motor drive control signal containing frequency control commands and torque compensation commands; controlling the motor to operate at the target adjustment frequency through the motor drive circuit, and controlling the massage head drive mechanism to make the output pressure value of the massage head linked and matched with the target adjustment frequency and the output torque adjustment coefficient. If the detected contact pressure value exceeds the preset safety pressure threshold, the motor frequency is forcibly reduced, and the pressure adjustment component is controlled to reduce the output pressure of the massage head. If the electromyography signal sensor continuously collects electromyography signals of the target muscle group and shows a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a gradual adjustment of muscle relaxation intensity.

2. The method according to claim 1, characterized in that, The step of analyzing the muscle stiffness and muscle fatigue parameters of the target muscle group based on the electromyographic signals includes: The electromyographic signal is subjected to noise reduction filtering to extract time-domain and frequency-domain feature values. According to the preset muscle hardness-feature value mapping table, the muscle hardness level of the target muscle group is determined by the root mean square value, integral electromyography value and median frequency in the time domain feature value; based on the amplitude decay rate and frequency drift of the electromyography signal, combined with the preset fatigue judgment rules, the fatigue level of the target muscle group is divided into mild fatigue, moderate fatigue and severe fatigue.

3. The method according to claim 1, characterized in that, The step of determining the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range includes: The effective frequency range is obtained by intersecting the motor rotation frequency range with the preset safe frequency range. Based on the midpoint of the effective frequency range, user history usage preference data, or current muscle fatigue level, a target adjustment frequency is selected from the effective frequency range. When the muscle fatigue level is severe fatigue, the target adjustment frequency is preferentially selected from the lower limit of the effective frequency range.

4. The method according to claim 1, characterized in that, The method of controlling the motor to operate at a target regulating frequency via a motor drive circuit includes: The motor speed feedback signal is collected in real time using the motor's built-in speed sensor; The speed feedback signal is compared with the target adjustment frequency by a closed-loop control algorithm, and the driving parameters of the motor drive circuit are dynamically adjusted to keep the motor speed stable within the error range of ±5% of the target adjustment frequency. The closed-loop control algorithm includes a proportional-integral-derivative control algorithm.

5. The method according to claim 1, characterized in that, The massage head drive mechanism is equipped with an electric push rod or an elastic pressure adjustment component. The control massage head drive mechanism enables the output pressure value of the massage head to be linked and matched with the target adjustment frequency and output torque adjustment coefficient, including: According to the preset pressure-frequency-torque linkage mapping table, obtain the target output pressure value corresponding to the target adjustment frequency and output torque adjustment coefficient; By controlling the extension and retraction stroke of the electric push rod or the compression amount of the elastic pressure adjustment component, the output pressure value of the massage head is adjusted to the target output pressure value. The linkage mapping table is obtained by experimentally calibrating the optimal relaxation pressure under different muscle hardness levels.

6. The method according to claim 1, characterized in that, If the detected contact pressure value exceeds a preset safety pressure threshold, a forced reduction mechanism for the motor frequency is triggered, controlling the pressure regulation component to reduce the output pressure of the massage head, including: When the contact pressure value exceeds 1.2 times the preset safety pressure threshold, a first-level downshift is triggered, reducing the current operating frequency of the motor by 20%, and controlling the pressure regulating component to reduce the output pressure to 70% of the current value; If the contact pressure value continues to exceed the safe pressure threshold for 500 milliseconds, a second-level downshift will be triggered, further reducing the motor frequency by 30% and decreasing the output pressure to 50% of the initial value. An overload warning will also be issued through the fascia gun's indicator module.

7. The method according to claim 1, characterized in that, If the electromyography (EMG) signal sensor continuously acquires EMG signals of the target muscle group that show a periodic decreasing trend, the motor rotation frequency is reduced and the output pressure is decreased to form a progressive adjustment of muscle relaxation intensity, including: The signal monitoring time window is set to 10-30 seconds. When the amplitude of the electromyographic signal is detected to show a stepwise decrease for more than 3 consecutive cycles within the time window, it is judged that the muscle is in the relaxation adaptation stage. Based on the current level of muscle fatigue, the motor rotation frequency is adjusted according to the preset frequency decrease gradient, and the output pressure of the massage head is reduced by the same proportional coefficient until the amplitude of the electromyographic signal tends to stabilize or reaches the preset minimum relaxation intensity threshold.

8. The method according to claim 1, characterized in that, The process of combining contact pressure values ​​and motion posture data to obtain the currently applicable motor rotation frequency range and corresponding output torque adjustment coefficient includes: The accelerometer analyzes the three-dimensional posture angle of the fascia gun to determine whether the current pressing direction of the massage head is perpendicular to the target muscle surface. When the tilt angle of the pressing direction exceeds a preset threshold, the contact pressure value is corrected and compensated according to the tilt angle to obtain an equivalent vertical pressure value. Based on the corrected equivalent vertical pressure value, muscle stiffness parameters, and fatigue parameters, the corresponding motor frequency range and torque adjustment coefficient are matched through a pre-trained adjustment rule library. The adjustment rule library is generated by training on muscle relaxation effect data under different pressing postures using machine learning algorithms.

9. A fascia gun adaptive frequency adjustment and muscle relaxation intensity control system, characterized in that, This is applied to a fascia gun, which includes a housing, a motor housed within the housing, a massage head drive mechanism connected to the motor, a motor drive circuit, a control module, a pressure sensor located on the massage head drive mechanism or the massage head, an electromyography (EMG) signal sensor located on the housing or the massage head, and an acceleration sensor for acquiring the motion state of the fascia gun; including: The coefficient acquisition unit is used to receive the pressure value of the massage head in contact with the human body collected by the pressure sensor, the electromyographic signal of the target muscle group collected by the electromyographic signal sensor, and the fascia gun movement posture data collected by the acceleration sensor; based on the electromyographic signal, it analyzes the muscle hardness parameters and muscle fatigue parameters of the target muscle group, and combines the contact pressure value and movement posture data to obtain the currently applicable motor rotation frequency range and the corresponding output torque adjustment coefficient. The linkage matching unit is used to determine the target adjustment frequency within a preset safe frequency range based on the motor rotation frequency range, and to generate a motor drive control signal based on the output torque adjustment coefficient. This includes: converting the output torque adjustment coefficient into a corresponding drive current correction parameter based on the motor's rated torque-current characteristic curve; adjusting the duty cycle or voltage amplitude of the reference drive signal using the drive current correction parameter, in conjunction with the reference drive signal corresponding to the target adjustment frequency, to generate a motor drive control signal containing frequency control commands and torque compensation commands; controlling the motor to operate at the target adjustment frequency through the motor drive circuit, and controlling the massage head drive mechanism to achieve linkage matching between the massage head's output pressure value and the target adjustment frequency and output torque adjustment coefficient. The intensity adjustment unit is used to trigger a forced reduction mechanism of motor frequency if the detected contact pressure value exceeds the preset safety pressure threshold, thereby controlling the pressure adjustment component to reduce the output pressure of the massage head; if the electromyography signal sensor continuously collects electromyography signals of the target muscle group showing a periodic weakening trend, the motor rotation frequency is reduced and the output pressure is reduced, forming a progressive muscle relaxation intensity adjustment.

Images