Flatfoot sole model generation system and device

Abstract

The invention relates to the technical field of medical assistance, and discloses a flatfoot sole model generation system and device, which comprises a plurality of air bag elements, a pressure data acquisition module, a motion capture module, a static data processing module, a dynamic data processing module, an execution control module and a model generation module, through the synergistic effect of the air bag element, the pressure data acquisition module and the static and dynamic data processing module, in combination with real-time inflation adjustment and foot arch state judgment, the plantar model adaptive to the foot characteristics of the user is dynamically generated, plantar pressure distribution can be monitored and adjusted in real time, the plantar model is dynamically generated according to the foot motion state of the user, and the user experience is improved. The method has the advantage of improving the flatfoot correction effect and the user experience.

Description

Technical Field

[0001] The present invention relates to the field of medical assistance technology, and in particular to a flat foot plantar model generation system. Background Art

[0002] During normal walking, the soles of the feet bear the body's weight and control balance and movement through contraction of the plantar muscles. If pressure distribution on the soles of the feet is irregular, the body requires increased control to maintain balance and stability during normal walking. Flat feet, also known as flatfoot deformity, is a condition in which the medial longitudinal arch of the foot is low or absent. This condition may be caused by congenital or acquired causes that lead to dysfunction of the foot and ankle muscles, tendons, or ligaments, or abnormal bone structure that prevents the arch from maintaining a normal foot arch. The incidence of flat feet is related to age, gender, and weight, with a higher incidence in men than in women, and a higher incidence in obese children. The condition can be divided into congenital flat feet and acquired flat feet, the latter also known as acquired flat feet. In flat feet, the tendons, ligaments, and small bones in the soles of the feet are unable to effectively support the body, resulting in a collapsed arch. When walking or standing, the weight load is unevenly distributed, and the center of gravity tends to tilt inward, leading to excessive pronation. Common flat feet can be divided into two types: flexible flat feet and rigid flat feet. If flexible flat feet are not corrected in the early stage, they will lead to hardening of the flexible flat feet and form irreversible lesions. Therefore, early physical therapy intervention for flexible flat feet is extremely important.

[0003] In the existing technology, some systems attempt to collect data through pressure sensor arrays. However, the data obtained in this way cannot verify the wearing effect, cannot determine whether it has a good correction effect, and is difficult to adapt to the changes in the physiological characteristics of the feet of different users. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a flat foot plantar model generation system for.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A flat foot plantar model generation system, comprising

[0007] Several airbag components,

[0008] A pressure data acquisition module collects the pressure in each airbag element to define it as pressure data.

[0009] A motion capture module, which acquires ankle motion data,

[0010] A static data processing module generates a static data acquisition signal to the pressure data acquisition module to obtain static pressure data, specifically the pressure data of the user in a standing state, and determines the coarse adjustment inflation volume of each airbag element based on a preset static pressure adjustment strategy.

[0011] A dynamic data processing module generates a dynamic data acquisition signal to the pressure data acquisition module and the motion capture module to obtain dynamic data, wherein the dynamic data includes dynamic pressure data and the ankle motion data. Based on a preset arch state judgment strategy, the module determines whether the current arch state meets the preset arch condition. If not, the module determines the fine-tuning inflation amount of each airbag element based on a preset dynamic pressure adjustment strategy. The operation is repeated until the current arch state meets the arch condition and a standard-reaching signal is generated.

[0012] an execution control module, wherein the execution control module controls the inflation element to inflate or deflate each airbag element based on the coarse adjustment inflation amount or the fine adjustment inflation amount,

[0013] A model generation module receives the standard-reaching signal and generates a plantar model.

[0014] Furthermore, the static pressure regulation strategy includes

[0015] Inflate each airbag component to the initial inflation volume,

[0016] The user stands on the airbag and measures the air pressure in each airbag component. After the air pressure stabilizes, the static pressure data is obtained.

[0017] The coarse adjustment inflation amount is calculated based on a preset inflation amount calculation model and the coarse adjustment inflation amount is obtained.

[0018] Furthermore, the calculation formula of the inflation volume calculation model includes

[0019]

[0020] ΔV c (i) = α·(P t (i)-P s (j))·V(i) max ,

[0021] Among them, P t (i) represents the target pressure value of the i-th airbag, N represents the total number of airbags, P s (j) represents the measured static pressure value of the jth airbag, k(i) represents the geometric distribution coefficient of the plantar area, ΔV c(i) represents the coarse inflation adjustment of the i-th airbag, α represents the preset inflation attenuation coefficient, V(i) max Indicates the preset maximum airbag volume.

[0022] Furthermore, the arch state judgment strategy includes

[0023] Guide users to move based on preset motion guidance commands,

[0024] The pressure data acquisition module continuously collects dynamic pressure data of each airbag component and generates a pressure change curve.

[0025] The motion capture module acquires ankle motion data, wherein the ankle motion data includes a three-dimensional motion angle of the ankle joint.

[0026] The current arch state is calculated based on a preset arch state calculation formula, wherein the current arch state parameters include the arch pressure fluctuation value and the ankle joint rotation angle.

[0027] The arch pressure fluctuation value is compared with the fluctuation pressure threshold, and the ankle joint flip angle is compared with the flip threshold. If the arch pressure fluctuation value is less than the fluctuation pressure threshold and the ankle joint flip angle is less than the flip threshold, it is considered that the preset arch condition is met; otherwise, it is considered that the preset arch condition is not met. The fluctuation pressure threshold is specifically the product of the corresponding target pressure value and the preset ratio.

[0028] Furthermore, the foot arch state calculation formula includes

[0029]

[0030] Φ=max(θ i (t)-θ e (t)),

[0031] Where S represents the arch pressure fluctuation value, P m (t) represents the real-time measurement value of dynamic pressure, P t ′ represents the target pressure value after static adjustment, t1 represents the start time of dynamic pressure measurement, t2 represents the end time of dynamic pressure measurement, T represents the difference between t2 and t1, Φ represents the maximum ankle rotation angle, θ i (t) represents the ankle inversion angle, θ e (t) represents the ankle eversion angle.

[0032] Furthermore, the calculation formula of the dynamic pressure regulation strategy includes

[0033]

[0034] where ΔV f(i) represents the fine-tuning inflation adjustment amount, β represents the safety correction factor, w1 represents the weight parameter of pressure stability, w2 represents the weight parameter of movement angle, b represents the preset normal ankle joint movement angle range, V(i) max Indicates the maximum allowable inflation volume of the airbag component.

[0035] Furthermore, the static data processing module is configured with a coarse adjustment parameter optimization strategy, which includes

[0036] Calculate the deviation between the coarse adjustment target and the fine adjustment result to define it as the coarse adjustment inflation volume deviation rate.

[0037] Compare the value of the coarse adjustment inflation volume deviation rate with the preset safety deviation threshold. If the value of the coarse adjustment inflation volume deviation rate exceeds the safety deviation threshold, calculate the error contribution of each coarse adjustment parameter.

[0038] The coarse adjustment parameters are updated based on the parameter update calculation formula.

[0039] Furthermore, the parameter update calculation formula includes

[0040] k g (i)′=k g (i)+n1·sign(E(i))·min(|S k (i)|,S0),

[0041]

[0042] where k g (i)′ represents the geometric distribution coefficient of the plantar area after updating, k g (i) represents the geometric distribution coefficient of the plantar area before updating, n1 represents the preset first learning rate, sign() represents the sign function, E(i) represents the difference between the coarse adjustment target and the fine adjustment result, min() represents the minimum value function, S k (i) represents the error contribution of the geometric distribution coefficient, S0 represents the preset maximum adjustment amount, α′ represents the inflation attenuation coefficient after the update, α represents the inflation attenuation coefficient before the update, n2 represents the preset second learning rate, S α (i) represents the error contribution of the inflation attenuation coefficient.

[0043] Furthermore, the error contribution calculation formula of the geometric distribution coefficient includes

[0044]

[0045] in, represents the sign of partial derivative, and P represents the mean static pressure.

[0046] A device for generating a flat foot plantar model is used in any of the flat foot plantar model generation systems described above, comprising an insole body, a plurality of independent airbags being provided in the middle of the insole body, and a cutting size guide line being provided on the insole body.

[0047] Beneficial effects of the present invention:

[0048] The present application provides a flat foot plantar model generation system and device, which dynamically generates a plantar model adapted to the user's foot characteristics through the synergistic effect of an airbag element, a pressure data acquisition module, and a static and dynamic data processing module, combined with real-time inflation adjustment and arch state judgment. It can monitor and adjust the plantar pressure distribution in real time, and dynamically generate a plantar model according to the user's foot movement state, which has the advantages of improving the flat foot correction effect and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a system architecture diagram of the flat foot plantar model generation system of the present invention;

[0050] Figure 2 Schematic diagram of the workflow of the flat foot plantar model generation system of the present invention;

[0051] Figure 3 It is a schematic diagram of the device for generating a flat foot plantar model in the present invention.

[0052] Reference numerals:

[0053] 1. Insole body; 2. Anterior arch sac; 3. Medial arch sac; 4. Lateral arch sac; 5. Injection hole; 6. Posterior arch sac. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a central component. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0057] In existing technologies, flatfoot correction devices usually rely on pressure sensor arrays to collect static plantar pressure data, but such devices cannot verify the actual effect of corrective interventions and it is difficult to determine whether the arch support has reached the expected physiological state. Traditional systems only adjust the support structure based on the initial pressure distribution and cannot adapt to the changes in the dynamic mechanical characteristics of the foot during walking, resulting in a deviation between the correction effect and the actual movement needs. A rehabilitation institution found in clinical tests that although the use of corrective insoles with fixed support parameters can improve the static arch morphology, patients still experience excessive ankle pronation during walking tests, indicating that static adjustment cannot meet dynamic biomechanical requirements.

[0058] To solve the above problems, the R&D team noticed that the arch function not only involves static support, but is also closely related to the coordinated contraction of the foot muscles during exercise. Through analysis, it was found that the existing technology lacks a dynamic feedback link, resulting in the inability of the correction parameters to match the movement state in real time. Further research found that there is a correlation between the ankle joint movement angle and the arch tension. If the plantar pressure changes and the ankle movement trajectory can be combined, a dynamic correction model can be established. Based on this idea, the designers tried to divide the pressure adjustment into two stages: static coarse adjustment and dynamic fine adjustment. By continuously monitoring the mechanical characteristics of the foot during exercise, a closed-loop adjustment mechanism was formed.

[0059] See Figure 1 and Figure 2 , this application proposes a flat foot plantar model generation system including several airbag elements, a pressure data acquisition module, a motion capture module, a static data processing module, a dynamic data processing module, an execution control module and a model generation module. The pressure data acquisition module collects the pressure data of each airbag element, and the motion capture module obtains the ankle motion data. The static data processing module generates a static data acquisition signal, obtains the static pressure data in the standing state and determines the coarse adjustment inflation volume of the airbag. The dynamic data processing module obtains the dynamic pressure data and ankle motion data, determines whether the arch state meets the conditions, and determines the fine adjustment inflation volume through iterative adjustment until it meets the standard. The execution control module controls the inflation and deflation of the airbag according to the inflation volume, and the model generation module generates a plantar model after meeting the standard.

[0060] The airbag elements are adjustable inflatable units distributed across key support areas on the sole of the foot. Specifically, they can be implemented using a silicone-wrapped microcavity structure, allowing localized support height to be adjusted through independent inflation and deflation. The pressure data acquisition module is a pressure sensing unit embedded within the airbags, typically implemented using a piezoresistive sensor array, used to quantify load distribution across various plantar regions. The motion capture module is a non-contact, three-dimensional motion tracking device, typically implemented using an infrared optical marker system, used to capture the three-dimensional motion trajectory of the ankle joint. The static data processing module is a signal-triggered computing unit, typically implemented using an embedded processor, that converts static pressure into initial inflation parameters using a preset algorithm. The dynamic data processing module is a control unit with real-time analysis capabilities, typically implemented using an FPGA chip, that assesses arch function by integrating pressure trends and motion angle data. The actuator control module is an air pressure control actuator, typically implemented using a combination of a micro air pump and solenoid valve, enabling precise control of the airbag volume. The model generation module is three-dimensional modeling software, typically implemented using a parametric modeling algorithm, that generates a customized plantar support surface based on the final stable state.

[0061] Specifically, the system's working process is divided into two stages: in the static adjustment stage, when the user stands, each airbag is inflated to the initial state according to the preset strategy. The pressure data is calculated to generate a rough adjustment of the inflation volume to form a basic support contour. After entering the dynamic adjustment stage, the system guides the user to perform standardized gait movements, and simultaneously collects the plantar pressure fluctuation curve and ankle joint rotation angle. When it is detected that the arch pressure fluctuation exceeds the threshold or the ankle is excessively inverted, the dynamic adjustment algorithm recalculates the required fine adjustment amount for each airbag, and through multiple inflation and deflation operations, the foot reaches a stable support state during movement. The final standard signal triggers the modeling program to convert the adjusted airbag shape into a three-dimensional plantar model.

[0062] Compared to existing technologies, which only perform single adjustments based on static pressure distribution, this solution introduces dynamic motion capture and iterative fine-tuning mechanisms to achieve real-time assessment of arch function during motion. Traditional systems lack correlation analysis between foot and ankle motion data and pressure changes. This solution uses ankle joint angle as an indirect indicator of arch tension, effectively improving the match between correction parameters and physiological needs. Furthermore, while existing technologies use fixed sensor arrays, this solution integrates the pressure sensing unit within the deformable airbag, more realistically reflecting the contact state of the support interface.

[0063] Through the above technical solution, this application can generate a personalized plantar model that meets actual sports needs based on the differences in the individual's biomechanical characteristics during static standing and dynamic walking. The system uses a closed-loop adjustment mechanism to ensure that the correction support maintains the static shape of the arch of the foot and adapts to the dynamic load changes during walking, solving the technical defect that traditional methods cannot verify the dynamic correction effect. The solution can also automatically optimize the airbag support parameters according to the real-time changes in the functional state of the foot during the user's exercise, thereby improving the adaptability of the flatfoot correction device to different sports scenarios.

[0064] The present application further proposes a flat foot plantar model generation system, which includes inflating each airbag element to an initial inflation volume, measuring the air pressure in each airbag element after the user stands on the airbag, obtaining static pressure data after the air pressure stabilizes, and calculating the coarse inflation adjustment amount based on the inflation volume calculation model to obtain the coarse inflation adjustment amount.

[0065] Among them, the initial inflation volume refers to the initial inflation volume of the airbag element, which can be determined by a preset benchmark value, such as an empirical value calculated based on the user's weight or foot size. Its function is to provide basic support for the user to stand. Static pressure data refers to the stable measurement value of the air pressure inside the airbag when the user is standing still. Specifically, it can be achieved by continuously collecting and averaging the pressure sensor. Its function is to provide a reliable benchmark for subsequent pressure adjustment. The inflation volume calculation model refers to a mathematical model that adjusts the inflation volume according to the difference between the static pressure data and the target pressure value. Specifically, a linear regression or gradient descent algorithm can be used to calculate the adjustment amount by combining the geometric distribution coefficient with the measured pressure value. Its function is to achieve optimal distribution of pressure distribution.

[0066] Specifically, when the user stands on the support surface formed by the airbag elements, each airbag is first inflated with an initial inflation volume to form a basic support structure. During the pressure stabilization stage, the pressure changes in each area are continuously monitored by the pressure sensor. For example, when the pressure fluctuation is less than 5% within 10 consecutive seconds, it is determined to be a stable state. Subsequently, the inflation volume calculation model performs a weighted calculation of the measured pressure based on the geometric distribution coefficient of the plantar area. For example, the distribution coefficient of the arch area can be set to 0.8 and the heel area to 1.2, thereby generating differentiated adjustment amounts for each airbag. This process converts the pressure measurement value into an accurate inflation instruction through mathematical calculations to ensure the rationality of the plantar pressure distribution in the static support state.

[0067] Compared to existing solutions, which often use fixed inflation patterns or single pressure thresholds, this solution, by introducing a baseline setting for the initial inflation volume and spatially weighted distribution of geometric distribution coefficients, effectively addresses the problem of traditional methods' insufficient consideration of the varying characteristics of plantar regions. For example, existing technologies fail to account for the differences in pressure transmission between collapsed arches and normal areas, while this solution achieves quantitative adaptation to the biomechanical characteristics of the plantar surface through the use of geometric distribution coefficients.

[0068] Through the above technical solution, this application can accurately establish a static support model that conforms to the user's foot anatomy, providing a precise initial benchmark for subsequent dynamic adjustments. The initial inflation volume avoids the adjustment lag caused by relying solely on sensor data, while the introduction of the geometric distribution coefficient improves the ability to distinguish the pressure sensitivity of different plantar regions, ultimately making the pressure distribution after rough adjustment closer to the ideal physiological state.

[0069] This application further proposes a calculation formula for the inflation volume calculation model, which includes:

[0070]

[0071] ΔV c (i) = α·(P t (i)-P s (j))·V(i) max ,

[0072] Among them, P t (i) represents the target pressure value of the i-th airbag, N represents the total number of airbags, P s (j) represents the measured static pressure value of the jth airbag, k(i) represents the geometric distribution coefficient of the plantar area, ΔV c (i) represents the coarse inflation adjustment of the i-th airbag, α represents the preset inflation attenuation coefficient, V(i) max Indicates the preset maximum airbag volume.

[0073] Among them, the target pressure value refers to the ideal pressure value of the airbag set by the preset static pressure adjustment strategy. Specifically, it can be calculated by using the plantar pressure distribution model combined with the user's weight data to guide the adjustment direction of the airbag inflation volume. The geometric distribution coefficient refers to the weight parameter that reflects the pressure distribution in different areas of the sole of the foot. Specifically, it can be obtained by matching the foot scanning data or the standard arch morphology database, and is used to adjust the pressure distribution ratio according to the difference in the user's plantar morphology. The inflation attenuation coefficient refers to the inflation efficiency correction parameter set according to the deformation characteristics of the airbag material. Specifically, it can be obtained by fitting the volume change curve under different air pressures through experimental testing, and is used to compensate for the inflation volume error caused by the elastic deformation of the airbag. The maximum volume of the airbag refers to the maximum inflation volume limit allowed for a single airbag. Specifically, it can be set by the airbag size and material strength parameters to avoid structural damage caused by excessive inflation.

[0074] The present application further proposes guiding the user to move based on preset motion guidance commands, the pressure data acquisition module continuously collects dynamic pressure data of each airbag component and generates a pressure change curve, the motion capture module obtains ankle motion data, the ankle motion data includes the three-dimensional motion angle of the ankle joint, and calculates the current arch state based on a preset arch state calculation formula. The current arch state parameters include the arch pressure fluctuation value and the ankle joint flip angle. The arch pressure fluctuation value is compared with the fluctuation pressure threshold, and the ankle joint flip angle is compared with the flip threshold. If the arch pressure fluctuation value is less than the fluctuation pressure threshold and the ankle joint flip angle is less than the flip threshold, it is deemed that the preset arch condition is met, otherwise it is deemed that the preset arch condition is not met. The fluctuation pressure threshold is specifically the product of the corresponding target pressure value and the preset ratio.

[0075] Among them, exercise guidance commands refer to pre-set voice or visual instructions that guide the user to perform standardized foot movements. For example, voice announcements or displays can be used to prompt the user to complete actions such as stepping, lateral movement, or standing on one leg. Their purpose is to ensure the standardization of user movements during dynamic data collection. Dynamic pressure data refers to the real-time changes in the internal pressure of each airbag element during the user's movement. Specifically, high-precision pressure sensors can be used to periodically collect data to reflect the force distribution characteristics of different areas of the sole under dynamic load. Ankle joint three-dimensional motion angle refers to the rotation angle data of the ankle joint in the sagittal, coronal, and transverse planes, obtained by an inertial measurement unit or optical capture device. For example, this can be achieved using a wearable device combining a gyroscope and accelerometer, and is used to assess foot stability during movement. The arch state calculation formula is an evaluation model established based on the mathematical relationship between the integral of pressure fluctuations and the ankle joint roll angle. Specifically, the pressure fluctuation value is obtained by integrating the square of the difference between the dynamic pressure and the target pressure in the time domain, and the maximum value of the ankle joint inversion and eversion angles is extracted as the roll angle indicator. Its purpose is to convert multidimensional data into quantifiable arch state parameters. The fluctuating pressure threshold is a determination boundary that is dynamically adjusted based on the target pressure value and a preset ratio, and can be, for example, 10%-30% of the target pressure value, and is used to determine the degree of abnormality in plantar pressure distribution.

[0076] Specifically, in the process of the user executing the exercise guidance command, the dynamic pressure data is formed into a time-varying curve through periodic sampling, for example, the pressure value of each airbag is recorded at a frequency of 10 times per second. At the same time, the ankle joint motion data is captured in real time by a multi-axis sensor, such as recording the instantaneous maximum value of the inversion angle and the eversion angle. The arch state calculation formula performs a weighted calculation on the pressure fluctuation value and the flip angle, for example, the pressure fluctuation integral is divided by the time length and added to the maximum flip angle to obtain a comprehensive evaluation value. After comparing the evaluation value with the preset threshold, if the pressure fluctuation value is lower than the threshold and the flip angle does not exceed the safe range, the arch state is determined to be up to standard, otherwise the dynamic adjustment of the airbag inflation volume is triggered. This process achieves optimal adjustment of the plantar pressure distribution through multiple iterations.

[0077] Compared to existing technologies, which rely solely on static pressure data for one-time adjustments and are unable to verify the corrective effect during dynamic movement, this solution, by introducing motion guidance and real-time data collection, can continuously monitor changes in the arch of the foot during active movement. Combined with ankle joint motion angle data, it establishes a multidimensional assessment model to effectively identify abnormal foot load distribution and joint stability issues. For example, existing technologies may ignore the tendency of the arch of the foot to collapse during walking, while this solution, through the coordinated analysis of pressure fluctuation curves and roll angles, can accurately capture arch abnormalities during dynamic movement.

[0078] Through the above technical solution, this application can dynamically evaluate the state of the arch of the foot during the user's actual exercise, improve the accuracy of judgment through a dual verification mechanism of pressure fluctuations and joint angles, and ensure the effectiveness of corrective measures in different exercise scenarios. For example, when the arch of the foot tends to collapse due to muscle fatigue while walking, the system can trigger the adjustment of the airbag inflation volume based on real-time data to prevent excessive inward rotation of the foot, thereby achieving dynamic correction support for flexible flat feet.

[0079] This application further proposes that the arch state calculation formula used in the arch state judgment strategy includes two calculation expressions, the first expression is the calculation of the dynamic pressure fluctuation value, and the second expression is the calculation of the ankle joint flip angle.

[0080]

[0081] Φ=max(θ i (t)-θ e (t)),

[0082] Where S represents the arch pressure fluctuation value, P m (t) represents the real-time measurement value of dynamic pressure, P t ′ represents the target pressure value after static adjustment, t1 represents the start time of dynamic pressure measurement, t2 represents the end time of dynamic pressure measurement, T represents the difference between t2 and t1, Φ represents the maximum ankle rotation angle, θ i (t) represents the ankle inversion angle, θ e (t) represents the ankle eversion angle.

[0083] Among them, the arch pressure fluctuation value refers to the difference between the dynamic pressure data and the static target pressure value to quantify the stability of the arch during movement. Specifically, a pressure sensor can be used to collect dynamic pressure data in real time, and the fluctuation range can be calculated in combination with an integral algorithm. For example, the average of the square of the pressure difference within a time window is calculated. This parameter is used to reflect the degree of abnormal fluctuation in the plantar pressure distribution. The maximum ankle joint rotation angle refers to the maximum offset of the ankle joint's inversion and eversion angles during movement. Specifically, a motion capture device can be used to collect three-dimensional angle data and determine it by comparing the peak values of the inversion and eversion angles. This parameter is used to evaluate the stability of the ankle joint during dynamic processes.

[0084] Specifically, when the user performs a preset motion, the dynamic pressure data is obtained by continuously collecting the pressure changes of each airbag component and calculating the degree of deviation from the static target pressure value to obtain the arch pressure fluctuation value; at the same time, the motion capture module obtains the inversion and eversion angles of the ankle joint, and selects the maximum value of the two as the flip angle evaluation index. When the pressure fluctuation value is lower than the preset threshold and the flip angle does not exceed the range of motion limit, it is determined that the arch state meets the correction target; if any indicator exceeds the threshold, the fine-tuning of the inflation volume of the airbag component is triggered. For example, when the user moves sideways, the arch pressure fluctuation value is calculated by integrating the square mean of the pressure difference within 0.5 seconds. At the same time, the ankle inversion angle and eversion angle are captured by the inertial sensor and the maximum value is screened to comprehensively judge whether further adjustment of the airbag support is needed.

[0085] Compared with existing technologies, existing solutions typically rely solely on static pressure data or a single motion angle for judgment, failing to simultaneously quantify the correlation between dynamic pressure fluctuations and joint rotation, leading to inaccurate verification of correction results. This solution, through dynamic pressure integral calculation and multi-dimensional ankle angle analysis, achieves a multi-dimensional dynamic assessment of arch condition, improving judgment accuracy and avoiding misjudgments caused by errors in a single indicator.

[0086] Through the above technical solution, the present application can monitor the coordinated changes of plantar pressure distribution and ankle joint movement in real time, accurately identify abnormal states of the arch of the foot during dynamic processes, and ensure the matching of corrective intervention with the user's actual exercise needs through fine-tuning of inflation volume feedback control, thereby solving the problems of inaccurate correction effect verification and insufficient adaptability in existing technologies.

[0087] The present application further proposes a dynamic pressure regulation strategy, the calculation formula of which includes the fine-tuning inflation adjustment amount equal to the safety correction factor multiplied by the product of the pressure stability weight parameter and the pressure fluctuation, plus the product of the movement angle weight parameter and the amplitude of the ankle joint rotation angle deviation from the normal range, divided by the maximum allowable inflation volume of the airbag element. The calculation formula of the dynamic pressure regulation strategy includes

[0088]

[0089] where ΔV f (i) represents the fine-tuning inflation adjustment amount, β represents the safety correction factor, w1 represents the weight parameter of pressure stability, w2 represents the weight parameter of movement angle, b represents the preset normal ankle joint movement angle range, V(i) max Indicates the maximum allowable inflation volume of the airbag component.

[0090] The safety correction factor is a protective parameter used to prevent excessive deformation during inflation adjustment. Specifically, it can be implemented as a fixed value based on the material's elastic modulus or airbag fatigue test data, limiting the amplitude of a single adjustment to prevent mechanical damage. The pressure stability weighting parameter quantifies the impact of dynamic pressure fluctuations on inflation adjustment. Specifically, it can be dynamically adjusted based on the plantar pressure distribution pattern classification results to balance the contribution of pressure changes to arch support during different movement phases. The motion angle weighting parameter is a parameter related to the correction effect of the ankle joint's motion angle. Specifically, it can be determined by analyzing the dynamic characteristics of the foot during the gait cycle and reflects the degree to which abnormal ankle joint motion affects arch collapse. The normal ankle joint motion angle range refers to the safety threshold for ankle inversion and varus angles determined in human physiology research. Specifically, it can be set using statistical data from healthy people in a clinical medicine database as a benchmark for determining whether foot motion is abnormal. The maximum allowable airbag element inflation volume is the limit volume value that prevents airbag rupture or compromises wearer comfort. Specifically, it can be set based on the tensile strength test results of the airbag material to ensure the safety of the adjustment operation.

[0091] Specifically, during the dynamic adjustment phase, when it is detected that the arch pressure fluctuation exceeds the threshold or the ankle joint rotation angle is abnormal, the system collects dynamic pressure and motion angle data in real time, and inputs the pressure fluctuation amplitude and angle deviation degree into the calculation formula. The pressure stability weight parameter is automatically adjusted according to the current pressure distribution pattern, for example, a higher weight is given during the heel contact phase and a lower weight is given during the propulsion phase. The motion angle weight parameter is dynamically corrected according to the degree of deviation of the ankle joint motion trajectory, and the parameter value is automatically increased when persistent inversion is detected. The safety correction coefficient is adaptively updated based on the airbag deformation record in the historical adjustment data, and the coefficient is automatically reduced when it is detected that a certain airbag frequently reaches the upper limit of the adjustment. The calculated fine-tuning inflation volume is used to fine-tune the specific airbag through the actuator, such as increasing the inflation volume of the airbag in the arch support area while reducing the pressure of the airbag on the lateral side of the forefoot, until the pressure fluctuation and motion angle return to the normal range.

[0092] Compared with existing technologies, traditional adjustment methods only perform linear compensation based on pressure data, without considering the dynamic impact of the ankle joint movement angle on the arch state, which can easily lead to local pressure overload or deviation in the direction of the correction force. However, this solution can simultaneously correct abnormal pressure distribution and biomechanical imbalance problems by introducing a comparison mechanism between the movement angle weight parameter and the normal range of motion of the ankle joint. In existing technologies, the safety correction coefficient usually uses a fixed value and cannot adapt to the individual differences in the elasticity of the soft tissue of the feet of different users. This solution realizes dynamic parameter optimization by combining airbag deformation feedback data, effectively preventing wearing discomfort caused by overinflation.

[0093] Through the above technical solution, this application can achieve coordinated adjustment of the arch support force and the ankle joint movement state, correcting abnormal biomechanical posture while ensuring balanced pressure distribution. By incorporating the movement angle deviation into the adjustment amount calculation, it is possible to accurately identify the arch collapse pattern caused by ankle inversion and strengthen the medial longitudinal arch support in a targeted manner. The dual protection mechanism of the safety correction coefficient and the maximum inflation volume limit is introduced to effectively avoid mechanical failure or user discomfort caused by excessive adjustment range, thereby improving system reliability and safety of use.

[0094] The present application further proposes configuring a coarse adjustment parameter optimization strategy in the static data processing module, which strategy includes calculating the coarse adjustment inflation volume deviation rate, comparing the deviation rate with the safety deviation threshold, calculating the error contribution when the deviation rate exceeds the threshold, and updating the coarse adjustment parameters based on the parameter update calculation formula.

[0095] Among them, the coarse adjustment inflation volume deviation rate refers to the degree of difference between the coarse adjustment target and the fine adjustment result, which can be implemented by the difference percentage algorithm, and is used to evaluate the error level in the initial adjustment stage. The safety deviation threshold refers to the preset allowable deviation range, which can be implemented by the empirical value range obtained from experimental data statistics, and is used to determine whether to trigger the parameter optimization mechanism. The error contribution refers to the weight of the influence of different parameters on the overall deviation, which can be implemented by using partial derivatives to calculate the sensitivity of each parameter to the pressure value, and is used to locate the parameter items that need to be adjusted first. The parameter update calculation formula refers to the mathematical relationship based on the error contribution adjustment coefficient, which can be implemented by the gradient descent method combined with constraints, and is used to dynamically optimize the geometric distribution coefficient and the inflation attenuation coefficient.

[0096] Specifically, after the system completes the coarse adjustment inflation operation and enters the dynamic adjustment stage, the accuracy of the initial adjustment is judged by comparing the deviation rate between the coarse adjustment target pressure value and the actual pressure value after fine adjustment. If the deviation rate exceeds the preset safety threshold, it indicates that there is room for optimization in the original parameter setting of the static data processing module. At this time, the system automatically calculates the contribution of the geometric distribution coefficient and the inflation attenuation coefficient to the deviation, and determines the adjustment priority of each parameter through partial derivative analysis. For example, when the error contribution of the geometric distribution coefficient is significantly higher than that of the inflation attenuation coefficient, gradient descent adjustment is performed on this parameter first, and the sign function is combined to control the adjustment direction, and the maximum adjustment amount is set to prevent overfitting. This achieves adaptive updating of the parameters, so that the subsequent coarse adjustment stage can more accurately approach the target pressure value.

[0097] Compared with existing technologies, traditional solutions rely solely on fixed parameter models during the static adjustment phase and are unable to optimize the calculation logic based on actual dynamic data feedback, resulting in time-consuming multiple fine-tuning operations. This solution establishes a quantitative model for parameter contribution, enabling the system to autonomously identify key error sources and make targeted corrections, significantly improving prediction accuracy during the coarse adjustment phase. For example, in cases with complex arch geometry, dynamically adjusting the geometric distribution coefficient can ensure that the pressure distribution more closely matches the biomechanical characteristics of the user's foot.

[0098] Through the above technical solution, this application effectively solves the problem of initial deviation accumulation caused by parameter fixation during the static adjustment phase, and can dynamically optimize the calculation model based on the actual correction effect. This reduces the number of iterations during the dynamic fine-tuning phase, shortens the model generation cycle, and improves the adaptation accuracy of the plantar pressure distribution, providing more stable correction support effects for users with different physiological characteristics.

[0099] This application further proposes a parameter update calculation formula including a geometric distribution coefficient and an inflation attenuation coefficient update method, the geometric distribution coefficient is updated by multiplying the difference value and the error contribution in combination with the first learning rate, and the maximum adjustment amount is limited, and the inflation attenuation coefficient is updated by multiplying the error contribution and the second learning rate. The parameter update calculation formula includes

[0100] k g (i)′=k g (i)+n1·sign(E(i))·min(|S k (i)|,S0),

[0101]

[0102] where k g (i)′ represents the geometric distribution coefficient of the plantar area after updating, k g (i) represents the geometric distribution coefficient of the plantar area before updating, n1 represents the preset first learning rate, sign() represents the sign function, E(i) represents the difference between the coarse adjustment target and the fine adjustment result, min() represents the minimum value function, S k (i) represents the error contribution of the geometric distribution coefficient, S0 represents the preset maximum adjustment amount, α′ represents the inflation attenuation coefficient after the update, α represents the inflation attenuation coefficient before the update, n2 represents the preset second learning rate, S α (i) represents the error contribution of the inflation attenuation coefficient.

[0103] Among them, the geometric distribution coefficient refers to a parameter used to reflect the pressure distribution characteristics of the plantar area. It can be calculated using a statistical model of plantar pressure distribution and used to adjust the inflation weights of airbags in different positions to adapt to different degrees of arch collapse. The inflation attenuation coefficient refers to the attenuation efficiency parameter of the pressure change during the inflation of the airbag. It can be obtained through regression analysis of historical inflation data and is used to balance the inflation speed and stability. The first learning rate and the second learning rate refer to the coefficients of the control parameter update step size. They can be set by the optimizer of the gradient descent method to avoid over-adjustment or under-adjustment problems. The error contribution refers to the degree of influence of the parameter on the coarse adjustment target deviation. It can be calculated using partial derivatives to identify key parameters and make priority adjustments. The maximum adjustment amount refers to the upper limit of a single update of the geometric distribution coefficient. It can be determined by a preset safety range to prevent system instability caused by parameter mutations.

[0104] Specifically, when the coarse-adjusted inflation deviation rate exceeds the safety deviation threshold, the system calculates the error contribution of the geometric distribution coefficient and the inflation attenuation coefficient, combining the learning rate and the difference value to generate updated parameters. The update of the geometric distribution coefficient uses a sign function to determine the adjustment direction and is progressively optimized based on the contribution and maximum adjustment limit. The inflation attenuation coefficient is dynamically corrected through a linear combination of the contribution and the learning rate. This process allows the coarse-adjustment parameters to be adaptively adjusted based on the actual fine-tuning results, thereby improving the initial pressure distribution accuracy in the subsequent coarse-adjustment stage and reducing the number of dynamic adjustments.

[0105] Compared with existing technologies, which typically use fixed parameters or manual experience to adjust the coarse-tuning strategy, it is difficult to adapt to the physiological characteristics of different users' feet, resulting in inaccurate initial pressure distribution and time-consuming fine-tuning. However, this solution uses an adaptive parameter update mechanism to dynamically optimize the coarse-tuning parameters based on the actual correction effect, effectively reducing the impact of individual differences on model generation.

[0106] Through the above technical solution, this application can improve the generation accuracy and efficiency of the plantar model, reduce the number of iterations in the fine-tuning stage by dynamically optimizing the coarse-tuning parameters, and at the same time ensure the safety of the airbag inflation volume adjustment process, avoiding foot discomfort or correction failure caused by parameter mutations, and is especially suitable for personalized correction needs in early intervention of flexible flat feet.

[0107] Furthermore, the error contribution calculation formula of the geometric distribution coefficient includes

[0108]

[0109] in, represents the sign of partial derivative, and P represents the mean static pressure.

[0110] The geometric distribution coefficient error contribution refers to the coefficient's sensitivity to the deviation rate of the coarse inflation adjustment. This can be achieved by calculating the partial derivative of the relationship between the mean static pressure and the geometric distribution coefficient, for example, using numerical or symbolic differentiation. The mean static pressure refers to the average pressure of each airbag element when the user is standing. This can be achieved by collecting and averaging the pressure in real time using a pressure sensor array.

[0111] Specifically, when the static data processing module executes the coarse-tuning parameter optimization strategy, it first determines the error contribution of the geometric distribution coefficient based on the deviation rate between the coarse-tuning target and the fine-tuning result. By calculating the partial derivative of the geometric distribution coefficient with respect to the average static pressure, the degree of its influence on the pressure distribution can be quantified, and its contribution to the coarse-tuning inflation deviation can be determined. This process enables the system to identify the sources of error in key parameters and dynamically adjust the geometric distribution coefficient through the parameter update formula, thereby optimizing the calculation logic of the coarse-tuning inflation.

[0112] In some embodiments, partial derivatives can be calculated using a finite difference method. For example, by slightly adjusting the geometric distribution coefficient, observing the change in the mean static pressure, and then approximating the partial derivative value by the ratio of the change to the adjustment. The mean static pressure can be calculated by taking a weighted average of the pressure values of all airbag elements, where the weights can be parameters such as the support area or position distribution of the airbag elements.

[0113] Compared with existing methods, which typically rely on fixed empirical values or static parameters to calculate inflation volume, this method limits model adaptability. By introducing error contribution calculation, this application dynamically evaluates and corrects errors in the geometric distribution coefficient, making the optimization process of coarse-tuning parameters adaptive and more accurately matching the physiological characteristics of different users' feet.

[0114] Through the above technical solution, this application can effectively reduce parameter deviations between the coarse and fine tuning stages, improving the accuracy of plantar model generation. By quantifying the error contribution of the geometric distribution coefficient, the system can adjust key parameters in a targeted manner, avoiding repeated adjustments caused by errors in preset parameters, thereby improving the efficiency of corrective interventions and reducing user adaptation costs.

[0115] A device for generating a flat foot sole model, please refer to Figure 3 , used for generating a flat foot sole model system as described above, comprising an insole body 1, wherein a plurality of independent air bags are provided in the middle of the insole body, and a cutting size guide line is provided on the insole body. Figure 3 There are four groups of middle airbag components, including a front arch sac 2, an inner arch sac 3, an outer arch sac 4, and a rear arch sac 6. An injection hole 5 is provided on each airbag.

[0116] Independent airbags refer to multiple, isolated inflatable units, which can be separated by elastic materials to form independent cavities. The inflation volume of each airbag can be independently adjusted to adapt the pressure to different areas of the sole of the foot. Cutting size guides refer to marking lines pre-printed or engraved on the edge of the insole body, which can be implemented as dotted lines or color-coded lines. They are used to guide users to cut the insole according to their own foot size to ensure that the insole fits the foot.

[0117] Specifically, the insole body supports the arch area through independent air cells located in the middle. The inflation state of each air cell is dynamically adjusted based on plantar pressure data, forming a support structure that adapts to the individual arch shape. Cutting size guides allow users to adjust the insole size according to the shape of the foot, for example, by cutting away excess material along the guide lines to match the length and width of the insole body to the actual size of the foot. This combination of the independent adjustment capabilities of the air cells and the size adaptation function of the cutting guides solves the problem of existing technologies being unable to adapt to the changing physiological characteristics of different users' feet.

[0118] Compared to existing insoles, which typically use fixed sizes or a single pressure-adjusting structure, they are unable to fine-tune the dynamic pressure distribution in the collapsed arch area and lack a cutting guide mechanism to accommodate different foot shapes. This application, however, utilizes a distributed design of independent air cells to enable precise pressure adjustment across the key arch areas. Furthermore, cutting guide lines simplify the user's ability to adjust the insole size, significantly improving the device's adaptability to different foot characteristics.

[0119] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that do not depart from the principles of the present invention are within the scope of protection of the present invention.

Claims

1. A system for generating a flat foot plantar model, characterized by: include Several airbag components, A pressure data acquisition module collects the pressure in each airbag element to define it as pressure data. A motion capture module, which acquires ankle motion data, A static data processing module generates a static data acquisition signal to the pressure data acquisition module to obtain static pressure data, specifically the pressure data of the user in a standing state, and determines the coarse adjustment inflation volume of each airbag element based on a preset static pressure adjustment strategy. A dynamic data processing module generates a dynamic data acquisition signal to the pressure data acquisition module and the motion capture module to obtain dynamic data, wherein the dynamic data includes dynamic pressure data and the ankle motion data. Based on a preset arch state judgment strategy, the module determines whether the current arch state meets the preset arch condition. If not, the module determines the fine-tuning inflation amount of each airbag element based on a preset dynamic pressure adjustment strategy. The operation is repeated until the current arch state meets the arch condition and a standard-reaching signal is generated. an execution control module, wherein the execution control module controls the inflation element to inflate or deflate each airbag element based on the coarse adjustment inflation amount or the fine adjustment inflation amount, The model generation module receives the standard-reaching signal and generates a plantar model.

2. The flatfoot plantar model generation system according to claim 1, characterized in that: The static pressure regulation strategy includes Inflate each airbag component to the initial inflation volume, The user stands on the airbag and measures the air pressure in each airbag component. After the air pressure stabilizes, the static pressure data is obtained. The coarse adjustment inflation amount is calculated based on a preset inflation amount calculation model and the coarse adjustment inflation amount is obtained.

3. The flatfoot plantar model generation system according to claim 2, characterized in that: The calculation formula of the inflation volume calculation model includes ΔV c (i)=α·(P t (i)-P s (j))·V(i) max , Among them, P t (i) represents the target pressure value of the i-th airbag, N represents the total number of airbags, P s (j) represents the measured static pressure value of the jth airbag, k(i) represents the geometric distribution coefficient of the plantar area, ΔV c (i) represents the coarse inflation adjustment of the i-th airbag, α represents the preset inflation attenuation coefficient, V(i) max Indicates the preset maximum airbag volume.

4. The flatfoot plantar model generation system according to claim 1, characterized in that: The arch state judgment strategy includes Guide users to move based on preset motion guidance commands, The pressure data acquisition module continuously collects dynamic pressure data of each airbag component and generates a pressure change curve. The motion capture module acquires ankle motion data, wherein the ankle motion data includes a three-dimensional motion angle of the ankle joint. The current arch state is calculated based on a preset arch state calculation formula, wherein the current arch state parameters include the arch pressure fluctuation value and the ankle joint rotation angle. The arch pressure fluctuation value is compared with the fluctuation pressure threshold, and the ankle joint flip angle is compared with the flip threshold. If the arch pressure fluctuation value is less than the fluctuation pressure threshold and the ankle joint flip angle is less than the flip threshold, it is considered that the preset arch condition is met; otherwise, it is considered that the preset arch condition is not met. The fluctuation pressure threshold is specifically the product of the corresponding target pressure value and the preset ratio.

5. The flatfoot plantar model generation system according to claim 4, characterized in that: The foot arch state calculation formula includes Φ=max(θ i (t)-θ e (t)), Where S represents the arch pressure fluctuation value, P m (t) represents the real-time measurement value of dynamic pressure, P t ′ represents the target pressure value after static adjustment, t1 represents the start time of dynamic pressure measurement, t2 represents the end time of dynamic pressure measurement, T represents the difference between t2 and t1, Φ represents the maximum ankle rotation angle, θ i (t) represents the ankle inversion angle, θ e (t) represents the ankle eversion angle.

6. The flatfoot plantar model generation system according to claim 5, characterized in that: The calculation formula of the dynamic pressure regulation strategy includes where ΔV f (i) represents the fine-tuning inflation adjustment amount, β represents the safety correction factor, w1 represents the weight parameter of pressure stability, w2 represents the weight parameter of movement angle, b represents the preset normal ankle joint movement angle range, V(i) max Indicates the maximum allowable inflation volume of the airbag component.

7. The flatfoot plantar model generation system according to claim 1, characterized in that: The static data processing module is configured with a coarse adjustment parameter optimization strategy, which includes Calculate the deviation between the coarse adjustment target and the fine adjustment result to define it as the coarse adjustment inflation volume deviation rate. Compare the value of the coarse adjustment inflation volume deviation rate with the preset safety deviation threshold. If the value of the coarse adjustment inflation volume deviation rate exceeds the safety deviation threshold, calculate the error contribution of each coarse adjustment parameter. The coarse adjustment parameters are updated based on the parameter update calculation formula.

8. The flatfoot plantar model generation system according to claim 1, characterized in that: The parameter update calculation formula includes k g (i)′=k g (i)+n1·sign(E(i))·min(|S k (i)|,S0), where k g (i)′ represents the geometric distribution coefficient of the plantar area after updating, k g (i) represents the geometric distribution coefficient of the plantar area before updating, n1 represents the preset first learning rate, sign() represents the sign function, E(i) represents the difference between the coarse adjustment target and the fine adjustment result, min() represents the minimum value function, S k (i) represents the error contribution of the geometric distribution coefficient, S0 represents the preset maximum adjustment amount, α′ represents the inflation attenuation coefficient after the update, α represents the inflation attenuation coefficient before the update, n2 represents the preset second learning rate, S α (i) represents the error contribution of the inflation attenuation coefficient.

9. The flatfoot plantar model generation system according to claim 7, characterized in that: The error contribution calculation formula of the geometric distribution coefficient includes: in, represents the sign of partial derivative, and P represents the mean static pressure.

10. A device for generating a flat foot plantar model, used in the flat foot plantar model generation system according to any one of claims 1 to 9, characterized in that: The insole comprises an insole body, a plurality of independent air bags are arranged in the middle of the insole body, and a cutting size guide line is arranged on the insole body.

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