Self-adaptive foot dynamic load distribution method and system

Abstract

The invention relates to the technical field of foot detection, in particular to a self-adaptive foot dynamic load distribution method and system. The method comprises the following steps: acquiring foot detection data and a patient case; analyzing the foot detection data, and determining a foot force line offset; establishing a biomechanical model according to the foot force line offset; according to the patient case and the biomechanical model, determining an adjustment priority and a pressure transfer path of a foot load; and generating a load distribution suggestion according to the pressure transfer path based on the adjustment priority. And the adjustment priority and the pressure transfer path of the foot load are determined, so that local overload or compensation failure caused by dependence on empirical rules can be avoided, and the risk of secondary injury caused by orthopedic intervention is reduced. A load distribution suggestion is generated, the problem that a numerical target and space execution are disjointed is solved, and geometric adaptation deviation caused by artificial experience design is avoided.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of foot detection, in particular to a self-adaptive foot dynamic load distribution method and system. BACKGROUND

[0002] In foot orthopedic diagnosis and treatment, accurate assessment of foot dynamic load distribution is the core basis for solving pathological problems such as force line deviation, rotational deformity and toe bone coordination abnormalities. Traditional methods mostly rely on static images or single modality data.

[0003] However, the existing foot orthotic scheme cannot truly reflect the mechanical state of the foot under the action of gravity. Therefore, how to reflect the dynamic biomechanical characteristics in motion has become a major defect of the foot orthotic scheme. SUMMARY

[0004] The application provides a self-adaptive foot dynamic load distribution method and system to solve the above problems.

[0005] In a first aspect, the application provides a self-adaptive foot dynamic load distribution method, which comprises: Obtaining foot detection data and patient cases; analyzing the foot detection data to determine the foot force line deviation amount; According to the foot force line deviation amount, a biomechanical model is established; According to the patient cases and the biomechanical model, the adjustment priority and pressure transfer path of the foot load are determined; Based on the adjustment priority, a load distribution suggestion is generated according to the pressure transfer path.

[0006] Through the scheme, the foot detection data and patient cases are obtained, which helps to eliminate the omission of biomechanical characteristics caused by single detection dimension and avoid neglecting the physiological tolerance limit of the patient. The foot detection data is analyzed to determine the foot force line deviation amount, which helps to overcome the defect that two-dimensional image measurement cannot capture three-dimensional dynamic deformation, so that the calculation result of the deviation amount is more consistent with the biomechanical response under the real load state. According to the foot force line deviation amount, a biomechanical model is established, which helps to eliminate the adaptive deviation of the standard template model and the real anatomical structure. According to the patient cases and the biomechanical model, the adjustment priority and pressure transfer path of the foot load are determined, which helps to avoid the local overload or compensation failure caused by relying on experience rules and reduce the secondary injury risk caused by orthotic intervention. Based on the adjustment priority, a load distribution suggestion is generated according to the pressure transfer path, which solves the problem of disconnection between numerical target and space execution and avoids the geometric adaptive deviation caused by artificial experience design.

[0007] Optionally, the foot detection data is obtained, comprising: Obtain the current load signal, analyze the load signal, and determine the load source; Based on the patient's medical records, determine whether the load-bearing source meets the testing requirements; If the conditions are met, obtain the CT scan results and pressure data; Analyze the CT scan results to determine the three-dimensional coordinates of the foot bones and the soft tissue deformation characteristics; Analyze the pressure data to determine the dynamic pressure distribution and dynamic changes in the limbs; The three-dimensional coordinates of the foot bones, the soft tissue deformation characteristics, the dynamic pressure distribution, and the dynamic changes of the limbs are determined as the foot detection data.

[0008] Optionally, analyzing the pressure data to determine dynamic pressure distribution and limb dynamic changes includes: Analyze the pressure data to determine the changes in pressure distribution; Based on the changes in pressure distribution, determine whether the current stage is dynamic detection. If it is in the state, then the change in pressure distribution is determined as the dynamic pressure distribution; Based on the three-dimensional coordinates of the foot bones, the center point of the calcaneus and the center point of the metatarsal heads are marked and determined; Based on the center point of the calcaneus and the center point of the metatarsal heads, the dynamic changes of the limbs are determined according to the changes in pressure distribution.

[0009] Optionally, determining the dynamic changes of the limb based on the center point of the calcaneus and the center point of the metatarsal heads, according to the changes in pressure distribution, includes: Based on the three-dimensional coordinates of the foot bones, the forefoot region, midfoot region, and heel region are determined; Based on the changes in pressure distribution, determine the changes in the forefoot region, midfoot region, and heel region; Based on the center point of the heel bone and the center point of the metatarsal head, and according to the changes in the forefoot region, midfoot region, and heel region, the tibial torsion angle and the ankle joint inversion or eversion angle are determined. The tibial torsion angle and the ankle joint inversion or eversion angle are defined as the dynamic changes of the limb.

[0010] Optionally, establishing a biomechanical model based on the foot force line offset includes: Analyze the dynamic pressure distribution to determine the rotational distortion angle; Analyze the dynamic changes of the limbs to determine the phalanx coordination parameters; A biomechanical model is established based on the foot force line offset, the rotational deformity angle, and the toe bone coordination parameters.

[0011] Optionally, establishing a biomechanical model based on the foot force line offset, the rotational deformity angle, and the phalanx coordination parameters includes: Based on the three-dimensional coordinates of the foot bones, the CT scan results are analyzed to determine the baseline force line; Based on the foot force line offset, determine the offset time and maximum offset distance of the foot pressure center trajectory relative to the reference force line; A dynamic force line offset index is established based on the offset time, the maximum offset distance, and the reference force line; Analyze the phalangeal synergistic parameters to determine the impact of foot load; A biomechanical model is established based on the dynamic force line offset index, the foot load effect, and the rotational deformity angle.

[0012] Optionally, determining the adjustment priority and pressure transfer path of foot load based on the patient's case and the biomechanical model includes: Based on the patient's medical record, the patient's initial condition is determined; Based on the biomechanical model, high-risk areas of the foot were identified; Analyze the high-risk areas of the foot to determine the pressure bearing potential of adjacent areas; Based on the pressure bearing potential, determine the adjustment priority of foot load; Based on the patient's initial condition, a pressure transfer pathway is determined.

[0013] Optionally, after establishing the dynamic force line offset index based on the offset time, the maximum offset distance, and the reference force line, the method further includes: Obtain the clinical validation dataset; Based on the clinical validation dataset, the mapping relationship between force line offset and actual disease severity was determined; Based on the mapping relationship, an index grading table is established; the index grading table includes a mild compensation range, a moderate risk range, and a severe damage range; when the dynamic force line offset index falls into the moderate risk range, a buffer area marker is added to the load allocation recommendation.

[0014] Optionally, the analysis of the high-risk areas of the foot and the determination of the pressure bearing potential of adjacent areas include: Obtain the historical maximum pressure bearing value of adjacent areas; Calculate the real-time structural strength coefficient of adjacent regions based on the biomechanical model; Based on the three-dimensional coordinates of the foot bones, the contact area ratio between adjacent areas and high-risk areas is calculated; Based on the historical maximum pressure bearing capacity, structural strength coefficient, and contact area ratio, a pressure bearing potential score is generated to determine the pressure bearing potential of adjacent areas.

[0015] Secondly, this application provides an adaptive foot dynamic load distribution system, the system comprising: The data analysis module is used to acquire foot detection data and patient cases; analyze the foot detection data to determine the foot force line offset; The model building module is used to build a biomechanical model based on the foot force line offset. The priority and path determination module is used to determine the adjustment priority and pressure transfer path of foot load based on the patient's case and the biomechanical model. A suggestion generation module is used to generate load allocation suggestions based on the adjustment priority and the pressure transfer path.

[0016] Optionally, when the data analysis module acquires foot detection data, it is used for: Obtain the current load signal, analyze the load signal, and determine the load source; Based on the patient's medical records, determine whether the load-bearing source meets the testing requirements; If the conditions are met, obtain the CT scan results and pressure data; Analyze the CT scan results to determine the three-dimensional coordinates of the foot bones and the soft tissue deformation characteristics; Analyze the pressure data to determine the dynamic pressure distribution and dynamic changes in the limbs; The three-dimensional coordinates of the foot bones, the soft tissue deformation characteristics, the dynamic pressure distribution, and the dynamic changes of the limbs are determined as the foot detection data.

[0017] Optionally, when the data analysis module analyzes the pressure data to determine the dynamic pressure distribution and dynamic changes in the limbs, it is used for: Analyze the pressure data to determine the changes in pressure distribution; Based on the changes in pressure distribution, determine whether the current stage is dynamic detection. If it is in the state, then the change in pressure distribution is determined as the dynamic pressure distribution; Based on the three-dimensional coordinates of the foot bones, the center point of the calcaneus and the center point of the metatarsal heads are marked and determined; Based on the center point of the calcaneus and the center point of the metatarsal heads, the dynamic changes of the limbs are determined according to the changes in pressure distribution.

[0018] Optionally, when the data analysis module determines the dynamic changes of the limb based on the center point of the calcaneus and the center point of the metatarsal heads, according to the changes in pressure distribution, it is used for: Based on the three-dimensional coordinates of the foot bones, the forefoot region, midfoot region, and heel region are determined; Based on the changes in pressure distribution, determine the changes in the forefoot region, midfoot region, and heel region; Based on the center point of the heel bone and the center point of the metatarsal head, and according to the changes in the forefoot region, midfoot region, and heel region, the tibial torsion angle and the ankle joint inversion or eversion angle are determined. The tibial torsion angle and the ankle joint inversion or eversion angle are defined as the dynamic changes of the limb.

[0019] Optionally, when the model building module builds a biomechanical model based on the foot force line offset, it is used for: Analyze the dynamic pressure distribution to determine the rotational distortion angle; Analyze the dynamic changes of the limbs to determine the phalanx coordination parameters; A biomechanical model is established based on the foot force line offset, the rotational deformity angle, and the toe bone coordination parameters.

[0020] Optionally, when the model building module establishes a biomechanical model based on the foot force line offset, the rotational deformity angle, and the phalanx coordination parameters, it is used for: Based on the three-dimensional coordinates of the foot bones, the CT scan results are analyzed to determine the baseline force line; Based on the foot force line offset, determine the offset time and maximum offset distance of the foot pressure center trajectory relative to the reference force line; A dynamic force line offset index is established based on the offset time, the maximum offset distance, and the reference force line; Analyze the phalangeal synergistic parameters to determine the impact of foot load; A biomechanical model is established based on the dynamic force line offset index, the foot load effect, and the rotational deformity angle.

[0021] Optionally, when the priority and path determination module determines the adjustment priority and pressure transfer path of the foot load based on the patient's case and the biomechanical model, it is used for: Based on the patient's medical record, the patient's initial condition is determined; Based on the biomechanical model, high-risk areas of the foot were identified; Analyze the high-risk areas of the foot to determine the pressure bearing potential of adjacent areas; Based on the pressure bearing potential, determine the adjustment priority of foot load; Based on the patient's initial condition, a pressure transfer pathway is determined.

[0022] Optionally, the adaptive foot dynamic load distribution system further includes a marker addition module for: Obtain the clinical validation dataset; Based on the clinical validation dataset, the mapping relationship between force line offset and actual disease severity was determined; Based on the mapping relationship, an index grading table is established; the index grading table includes a mild compensation range, a moderate risk range, and a severe damage range; when the dynamic force line offset index falls into the moderate risk range, a buffer area marker is added to the load allocation recommendation.

[0023] Optionally, when the priority and path determination module analyzes the high-risk area of ​​the foot and determines the pressure bearing potential of adjacent areas, it is used for: Obtain the historical maximum pressure bearing value of adjacent areas; Calculate the real-time structural strength coefficient of adjacent regions based on the biomechanical model; Based on the three-dimensional coordinates of the foot bones, the contact area ratio between adjacent areas and high-risk areas is calculated; Based on the historical maximum pressure bearing capacity, structural strength coefficient, and contact area ratio, a pressure bearing potential score is generated to determine the pressure bearing potential of adjacent areas. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart of an adaptive foot dynamic load distribution method provided in an embodiment of this application; Figure 3 This is a schematic diagram of an adaptive foot dynamic load distribution system provided in an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] Existing foot orthotic solutions fail to accurately reflect the biomechanical state of the foot under gravity. Therefore, how to reflect the dynamic biomechanical characteristics during movement has become a significant shortcoming of foot orthotic solutions.

[0030] Based on this, this application provides an adaptive dynamic load distribution method and system for the foot, which involves acquiring foot detection data and patient cases; analyzing the foot detection data to determine the foot force line offset; establishing a biomechanical model based on the foot force line offset; determining the adjustment priority and pressure transfer path of the foot load based on the patient cases and the biomechanical model; and generating load distribution suggestions based on the adjustment priority and pressure transfer path. Acquiring foot detection data and patient cases helps eliminate the omission of biomechanical features caused by a single detection dimension and avoids ignoring the patient's physiological tolerance limits. Analyzing the foot detection data to determine the foot force line offset helps overcome the deficiency of two-dimensional imaging measurements in capturing three-dimensional dynamic deformation, making the offset calculation results more consistent with the biomechanical response under actual load-bearing conditions. Establishing a biomechanical model based on the foot force line offset helps eliminate the adaptation deviation between standard template models and actual anatomical structures. Determining the adjustment priority and pressure transfer path of the foot load based on patient cases and the biomechanical model helps avoid local overload or compensatory failure caused by relying on empirical rules, and reduces the risk of secondary injury caused by orthopedic intervention. Based on the adjustment priority and the pressure transfer path, load allocation suggestions are generated to solve the problem of the disconnect between numerical objectives and spatial execution, and to avoid geometric adaptation deviations caused by manual experience design.

[0031] Figure 1 This is a schematic diagram illustrating an application scenario of this application, where the method provided in this application is applied during foot orthotics.

[0032] Specifically, the method provided in this application can be applied to any server. The server interacts with a 3D foot scanner to acquire foot detection data and retrieves patient cases stored on the server. The foot detection data is analyzed to determine the foot force line offset, and a biomechanical model is established based on this offset. Based on the patient cases and the biomechanical model, the adjustment priority and pressure transfer path of the foot load are determined, helping to avoid local overload or compensatory failure caused by relying on empirical rules and reducing the risk of secondary injury caused by orthopedic intervention. Based on the adjustment priority and the pressure transfer path, load distribution suggestions are generated to solve the problem of the disconnect between numerical objectives and spatial execution, and to avoid geometric adaptation deviations caused by manual experience-based design. Specific implementation methods can be found in the following embodiments.

[0033] Figure 2 This is a flowchart illustrating an adaptive foot dynamic load distribution method according to an embodiment of this application. The method of this embodiment can be applied to servers in the above-described scenarios. Figure 2 As shown, the method includes: S201. Obtain foot detection data and patient medical records; analyze the foot detection data to determine the foot force line offset. Foot detection data can be a comprehensive dataset consisting of static foot morphology data, dynamic pressure time-series distribution and temperature field data, combined with the patient's foot history and systemic disease data in the electronic medical record.

[0034] Patient case records can be a collection of clinical data including a history of foot disease, information on associated systemic diseases, records of previous use of orthotics, and limitations of joint range of motion.

[0035] Foot force line offset can be the real-time deviation angle and displacement between the line connecting the joint rotation center and the ideal force line when the foot is dynamically bearing weight.

[0036] Specifically, foot detection data is obtained by using static arch morphology data acquired through a 3D foot scanner, temporal distribution of plantar pressure during movement collected by a dynamic gait analyzer, and foot temperature field data recorded by a thermal imager. Subsequently, patient medical records are obtained by using foot history, systemic disease correlation data, and previous orthotic device usage records recorded in electronic medical records. Based on classical multi-rigid-body dynamics theory, the foot skeleton is decomposed into several rigid body units, connected by joint hinges, and a multi-rigid-body dynamics model is established according to the Newton-Euler equations. Using the multi-rigid-body dynamics model, the deviation angle and displacement of the line connecting the instantaneous rotation centers of each joint of the foot in the sagittal, coronal, and horizontal planes from the ideal force line from the calcaneal tuberosity to the center of the second metatarsal head are determined, i.e., the foot force line offset.

[0037] S202. Establish a biomechanical model based on the foot force line offset. Biomechanical models can be digital multibody dynamics models.

[0038] Specifically, based on medical image segmentation algorithms, skeletal contours are extracted from foot detection data to generate a patient-specific skeletal mesh model, annotating anatomical landmarks such as the medial or lateral malleolus and the tuberosity of the navicular bone. Then, based on thermal imaging temperature field and pressure distribution data, the elastic modulus distribution of the plantar fascia and fat pad is calculated through finite element inversion. Subsequently, based on the range of motion constraints in the patient's case, corresponding degree of freedom constraints are set in the patient-specific skeletal mesh model. Finally, using the foot force line offset as boundary conditions, the stress-strain distribution of each foot structure during the gait cycle is solved, thereby constructing a biomechanical model.

[0039] S203. Based on patient cases and biomechanical models, determine the priority of foot load adjustment and pressure transfer path; Foot load can be the sum of external pressure and internal stress that the foot bears during a static standing or dynamic gait cycle.

[0040] Priority adjustments can be used to determine the order and weight of foot load redistribution.

[0041] The pressure transfer path can be the optimal pressure transmission path from high-load areas to low-load areas in the plantar pressure network.

[0042] Specifically, the priority adjustment is constructed as follows: First, contraindicated adjustment areas are determined based on acute injury markers in the case; second, anatomical sites where pressure exceeds the threshold are prioritized for relief based on the joint contact pressure peak ranking output by the biomechanical model; and third, weighting coefficients are set for foot function requirements in combination with the patient's movement habits.

[0043] Generate pressure transfer path: First, starting from the high pressure accumulation area of ​​the metatarsal head, and taking the heel buffer zone and unmarked risk metatarsal areas as potential target points, establish an adjacency matrix of the plantar pressure distribution map; then, using the clinically validated improved Dijkstra algorithm, through dual weighting factors, dynamic pruning rules and real-time feedback mechanism, search for the optimal pressure transmission path from high-risk areas to low-load areas.

[0044] S204. Based on the adjustment priority, generate load allocation suggestions according to the pressure transfer path.

[0045] Load allocation recommendations can be generated from instructional outputs based on adjustments to priorities and load transfer paths.

[0046] Specifically, based on the adjustment priority, the target pressure value and allowable fluctuation range of each area of ​​the sole are marked; then, combined with the pressure transfer path, a customized intervention plan is designed and associated with the expected improvement indicators; thus generating load distribution suggestions.

[0047] This approach, by acquiring foot detection data and patient case studies, helps eliminate omissions of biomechanical features caused by single-dimensional detection and avoids overlooking patients' physiological tolerance limits. Analyzing foot detection data and determining foot force line offset helps overcome the limitation of two-dimensional imaging measurements in capturing three-dimensional dynamic deformation, making the offset calculation results more closely reflect the biomechanical response under actual load-bearing conditions. Based on the foot force line offset, a biomechanical model is established, helping to eliminate the adaptation deviation between standard template models and actual anatomical structures. Based on patient case studies and the biomechanical model, the adjustment priority and pressure transfer path of foot load are determined, helping to avoid local overload or compensatory failure caused by relying on empirical rules, and reducing the risk of secondary injury caused by orthopedic intervention. Based on the adjustment priority and pressure transfer path, load distribution suggestions are generated, solving the problem of the disconnect between numerical targets and spatial execution, and avoiding geometric adaptation deviations caused by manual experience-based design.

[0048] In some embodiments, the current weight-bearing signal is acquired, the weight-bearing signal is analyzed, and the weight-bearing source is determined; based on the patient's case, it is determined whether the weight-bearing source meets the detection requirements; if it does, CT scan results and pressure data are acquired; the CT scan results are analyzed to determine the three-dimensional coordinates of the foot bones and the soft tissue deformation characteristics; the pressure data is analyzed to determine the dynamic pressure distribution and limb dynamic changes; the three-dimensional coordinates of the foot bones, the soft tissue deformation characteristics, the dynamic pressure distribution, and the limb dynamic changes are determined as foot detection data.

[0049] The load signal can be a timing signal of plantar pressure.

[0050] The load-bearing source can be any type of mechanical action that causes the foot to bear load.

[0051] The testing requirement can be a preset testing trigger condition in the patient's electronic medical record.

[0052] CT scan results can be a sequence of tomographic images of the foot obtained through CT scans.

[0053] Pressure data can be the spatiotemporal distribution data of pressure in each zone of the sole of the foot.

[0054] The three-dimensional coordinates of the foot bones can be the spatial coordinates of anatomical landmarks in a three-dimensional bone model.

[0055] Soft tissue deformation characteristics can be the deformation parameters of soft tissues such as plantar fascia and fat pad under weight-bearing conditions.

[0056] Dynamic pressure distribution can be the temporal variation and spatial transmission path of pressure intensity in each zone of the foot during the gait cycle.

[0057] Limb dynamic changes can be parameters such as joint angles, movement trajectories, and torque changes during foot movements.

[0058] Specifically, a high-frequency pressure sensor array embedded in the detection platform collects weight-bearing signals from each anatomical region of the foot in real time. Then, based on the spatiotemporal distribution characteristics of the weight-bearing signals, the weight-bearing sources in the current gait cycle are analyzed. Next, clinical testing requirements from patient records are retrieved, and the weight-bearing sources and pressure peak ranges are logically matched with the testing requirements to determine if the weight-bearing sources meet the testing needs. When the weight-bearing sources meet the testing needs, a CT scanner is triggered to perform a spiral scan of the foot, acquiring the CT scan results; simultaneously, a pressure-sensitive gait analysis platform is activated to sample and record pressure data from each region of the foot. Then, Otsu thresholding and morphological operations are used to process the CT images, extracting the skeletal regions. Three-dimensional morphological closure operations are combined to fill in holes and reconstruct the skeletal point cloud model. An ICP registration algorithm using iterative nearest-point search and singular value decomposition is used to align the skeletal point cloud model with the standard anatomical coordinate system. Anatomical landmarks such as the medial malleolus, lateral malleolus, and scaphoid tuberosity are marked in the skeletal point cloud model, and the three-dimensional coordinates of the foot bones are calculated.

[0059] Based on a non-rigid registration algorithm, the plantar fascia strain field is calculated by matching CT images with ultrasound elastography data under load using the gradient descent method. Parameters such as fat pad compression ratio and arch deformation gradient are obtained through finite element interpolation, and soft tissue deformation characteristics are constructed based on these parameters.

[0060] Wavelet packet decomposition was performed on the pressure time-series data to determine the gait cycle and extract the pressure distribution differences between the support and swing phases within the gait cycle. Combining this with foot angular velocity data recorded by the inertial measurement unit (IMU), the ankle joint torque variation curve was inversely derived using the inverse dynamics standard formula and the foot angular velocity and center-of-mass acceleration recorded by the IMU. Finally, a data verification algorithm was used to align the skeletal three-dimensional coordinates, soft tissue deformation features, dynamic pressure distribution, and limb dynamic changes with timestamps, thereby generating foot detection data.

[0061] This solution acquires and analyzes the current load signal to identify the load source, helping to avoid resource waste caused by unnecessary testing. Based on patient cases, it determines whether the load source meets testing requirements, ensuring testing is only initiated when actually needed, improving clinical relevance and reducing invalid data collection. If it does, CT scan results and pressure data are acquired, helping to avoid data misalignment due to time-series differences. Analyzing CT scan results determines the three-dimensional coordinates of the foot bones and soft tissue deformation characteristics, providing geometric constraints for the biomechanical model with rigid structures, reflecting the cushioning performance and potential injury risk of soft tissues under load. Analyzing pressure data determines dynamic pressure distribution and limb dynamic changes, revealing areas of abnormal load concentration, and aiding in the diagnosis of motor dysfunction. The three-dimensional coordinates of the foot bones, soft tissue deformation characteristics, dynamic pressure distribution, and limb dynamic changes are defined as foot testing data, ensuring consistency in data format and semantics throughout the technical process.

[0062] In some embodiments, pressure data is analyzed to determine changes in pressure distribution; based on changes in pressure distribution, it is determined whether the current state is in a dynamic detection phase; if so, the changes in pressure distribution are determined as dynamic pressure distribution; the center point of the helix and the center point of the metatarsal heads are marked and determined based on the three-dimensional coordinates of the foot bones; based on the center points of the helix and the metatarsal heads, the dynamic changes of the limb are determined according to the changes in pressure distribution.

[0063] Changes in pressure distribution can be seen as the dynamic characteristics of the pressure intensity in each region of the plantar pressure data as it changes over time and spatially.

[0064] The dynamic detection phase can be the effective gait cycle time period.

[0065] The center point of the calcaneus can be the center point of the posterior articular surface of the calcaneus.

[0066] The center point of the metatarsal head can be the centroid of the distal articular surface of the first to fifth metatarsals.

[0067] Specifically, wavelet transform filtering is applied to the pressure data to remove high-frequency noise and retain the low-frequency pressure components related to the gait cycle, generating the denoised pressure distribution change.

[0068] Then, the displacement velocity of the pressure center trajectory after denoising is calculated: First, the pressure value of each zone of the sole of the foot is extracted at each sampling time, and the pressure weighted center coordinates are calculated; then, using the pressure weighted center coordinates, the displacement distance of the pressure center trajectory between adjacent sampling points is calculated based on the timestamp sequence, and the instantaneous velocity is obtained by combining the sampling interval.

[0069] Based on the average velocity of consecutive sampling points, it is determined whether the current stage is dynamic detection. If the average velocity of consecutive sampling points is too high, it is determined that the current stage is dynamic detection. Subsequently, the pressure distribution changes within the dynamic detection stage are captured and segmented into gait cycles, with each cycle consisting of a single period from heel strike to the second heel strike on the same side. Then, the pressure data within each cycle is normalized to generate a standardized dynamic pressure distribution.

[0070] Then, the 3D coordinate data of the foot skeleton reconstructed by CT was used to perform anatomical landmark annotation in the 3D model: First, the geometric center of the posterior articular surface of the calcaneus was taken, and the center point of the calcaneus was calculated using the coordinates after ICP registration; then, the centroids of the distal articular surfaces of the first to fifth metatarsal bones were taken, and the center points of the metatarsal heads were determined using a morphological skeleton extraction algorithm. Subsequently, the center point of the calcaneus was used as the origin of the coordinate system, and the line connecting the center points of the metatarsal heads was used as the forefoot reference axis.

[0071] Then, the trajectory of the pressure center in the dynamic pressure distribution change is projected onto the coordinate system, and the lateral offset and anterior-posterior displacement rate of the pressure center trajectory relative to the reference axis are calculated; thus, a rigid body motion model of the foot is established; finally, based on the rigid body motion model of the foot, combined with the foot angular velocity data of the inertial measurement unit, the ankle joint torque change curve is derived by inverse dynamics formula, and the dynamic change of the limb is output.

[0072] This method analyzes pressure data and determines pressure distribution changes, providing fundamental data for determining the dynamic detection phase. Based on pressure distribution changes, it determines whether the current state is in a dynamic detection phase, helping to avoid misclassifying non-gait movements as valid dynamic phases. If so, the pressure distribution changes are identified as dynamic pressure distribution, helping to eliminate the influence of individual weight differences on the absolute pressure value. Using the three-dimensional coordinates of the foot bones, the center points of the calcaneus and metatarsal heads are marked and determined, providing spatial reference for determining limb dynamic changes. Based on the center points of the calcaneus and metatarsal heads, and according to pressure distribution changes, the dynamic changes of the limb are determined, helping to quantify the rigid body posture changes and joint biomechanical parameters of the foot during movement, reflecting the kinematic and dynamic characteristics of the limb.

[0073] In some embodiments, the forefoot region, midfoot region, and heel region are determined based on the three-dimensional coordinates of the foot bones; changes in the forefoot region, midfoot region, and heel region are determined based on changes in pressure distribution; the tibial torsion angle and ankle inversion or eversion angle are determined based on the center point of the calcaneus and the center point of the metatarsal heads, according to the changes in the forefoot region, midfoot region, and heel region; and the tibial torsion angle and ankle inversion or eversion angle are determined as dynamic changes in the limb.

[0074] The forefoot region can be the anatomical area from the center of the metatarsal head to the tip of the toe, corresponding to the power output area of ​​the foot's propulsive movements.

[0075] The midfoot region can be the arch area between the center point of the heel bone and the center point of the metatarsal head, corresponding to the core weight-bearing area of ​​foot support and cushioning.

[0076] The heel area can be the area covered by the heel extending backward from the center point of the heel bone, corresponding to the stability bearing area of ​​the foot when it first touches the ground.

[0077] Changes in the forefoot region can be a set of quantitative characteristic parameters, including the pressure peak displacement rate, center of gravity offset, and intensity fluctuations corresponding to the forefoot region.

[0078] Variations in the midfoot region can be a set of quantitative characteristic parameters, including the direction of pressure center of gravity shift, pressure distribution uniformity, and fluctuation frequency corresponding to the midfoot region.

[0079] The change in the follow-up region can be a set of quantitative characteristic parameters of the pressure peak decay rate, pressure center offset trajectory, and intensity change corresponding to the follow-up region.

[0080] Tibial torsion angle can be the angular displacement of the tibia around this axis.

[0081] The angle at which the foot tilts inward within the ankle joint can be considered.

[0082] The eversion angle can be the angle at which the foot tilts outward.

[0083] Specifically, based on the three-dimensional coordinates of the foot bones, the spatial regions of the sole are divided by anatomical landmarks: First, the space from the center point of the metatarsal head to the tip of the toe is defined as the forefoot region; second, the arch region between the center point of the calcaneus and the center point of the metatarsal head is defined as the midfoot region; and then, the area covered by the heel extending backward from the center point of the calcaneus is defined as the heel region.

[0084] Pressure data subsets corresponding to the forefoot, midfoot, and heel regions are extracted from the dynamic pressure distribution. The peak pressure displacement rate, pressure center of gravity offset, and pressure intensity fluctuation frequency within each subset are calculated to generate dynamic pressure characteristic parameters for each region. Subsequently, the dynamic pressure characteristic parameters of the forefoot, midfoot, and heel regions are mapped to a skeletal reference coordinate system with the calcaneal center point as the origin and the line connecting the metatarsal head centers as the reference axis. Then, the inversion / vulcanization angle in the coronal plane of the ankle joint is inferred from the spatial angle change between the pressure center of gravity offset direction and the skeletal reference axis. Simultaneously, the axial torsion angle of the tibia around the reference axis is calculated using the phase difference between the peak pressure displacement rates of the forefoot and heel regions, yielding the tibial torsion angle. Finally, the tibial torsion angle and the ankle inversion / vulcanization angle are output as dynamic changes in the limb.

[0085] This scheme, based on the three-dimensional coordinates of the foot bones, determines the forefoot, midfoot, and heel regions, helping to clarify the boundaries of these regions and providing a spatial benchmark for extracting dynamic pressure features. By determining the changes in pressure distribution in the forefoot, midfoot, and heel regions, it helps to reveal the differences in plantar pressure transmission paths at different chronological stages, providing data support for skeletal motion correlation analysis. Based on the center points of the calcaneus and metatarsal heads, and according to the changes in the forefoot, midfoot, and heel regions, it determines the tibial torsion angle and the ankle joint's varus or valgus angle, helping to reflect the coronal stability of the ankle joint and characterize the rotational amplitude of the tibia around its reference axis. Defining the tibial torsion angle and the ankle joint's varus or valgus angle as dynamic changes in the limbs reflects the influence of foot movements on the lower limb alignment, providing an objective basis for assessing gait abnormalities.

[0086] In some embodiments, dynamic pressure distribution is analyzed to determine the rotational deformity angle; dynamic changes in the limbs are analyzed to determine the phalangeal coordination parameters; and a biomechanical model is established based on the foot force line offset, rotational deformity angle, and phalangeal coordination parameters.

[0087] The rotational distortion angle can be the maximum deviation angle between the offset trajectory of the plantar pressure center and the reference force line axis.

[0088] Toe bone coordination parameters can be quantitative parameters that characterize the efficiency of multi-regional biomechanical coordination of the foot and the functional status of interdigital linkage.

[0089] Specifically, the dynamic pressure distribution of the plantar contact surface is extracted; based on the three-dimensional coordinate system of the foot bones, the offset vector of the pressure distribution center point relative to the metatarsal head center point is calculated; then, based on the direction and amplitude of the offset vector, the rotational deformity angle is determined through geometric projection relationship.

[0090] The study acquires temporal variation data of tibial torsion angle and ankle inversion / valgus angle. Based on this data, it calculates synchronicity indices such as phase delay and intensity correlation coefficient of pressure response in each phalanx region. Then, based on the coupling relationship between these synchronicity indices and ankle angle changes, it generates phalanx synergistic parameters. Finally, it uses foot force line offset as the core input to correlate rotational deformity angle with phalanx synergistic parameters. A kernel method from machine learning is employed as a nonlinear mapping framework, and a biomechanical model is established through a nonlinear mapping algorithm.

[0091] This approach analyzes dynamic pressure distribution and determines rotational deformity angles, helping to reflect the geometric characteristics of pressure imbalance on the foot contact surface and providing quantitative input for three-dimensional mechanical distortion of the foot in biomechanical models. Analyzing limb dynamic changes and determining phalangeal coordination parameters helps reveal the joint linkage mechanism during ankle-foot complex movement and quantifies the mechanical coordination efficiency of several foot regions. Based on foot force line offset, rotational deformity angles, and phalangeal coordination parameters, a biomechanical model is established, which helps eliminate abnormal mechanical transmission paths caused by arch collapse or skeletal deformities, and outputs quantifiable levels of foot functional abnormalities.

[0092] In some embodiments, based on the three-dimensional coordinates of the foot bones, CT scan results are analyzed to determine the baseline force line; based on the foot force line offset, the offset time and maximum offset distance of the plantar pressure center trajectory relative to the baseline force line are determined; based on the offset time, maximum offset distance, and baseline force line, a dynamic force line offset index is established; phalangeal synergy parameters are analyzed to determine the influence of foot load; based on the dynamic force line offset index, the influence of foot load, and rotational deformity angle, a biomechanical model is established.

[0093] The baseline force line can be a static force line generated based on the center point of the calcaneus and the center point of the second metatarsal head marked on CT images.

[0094] The trajectory of the plantar pressure center can be a continuous path of the center of gravity of the plantar pressure distribution changing over time during dynamic foot movement.

[0095] Offset time can be the cumulative time during which the trajectory of the plantar pressure center deviates from the baseline.

[0096] The maximum offset distance can be the extreme value of the spatial deviation of the center trajectory of the plantar pressure relative to the baseline force line.

[0097] The dynamic force line offset index can be a composite parameter that combines offset time and maximum offset distance.

[0098] Foot load can be caused by changes in the distribution characteristics of foot mechanical load.

[0099] Specifically, in the three-dimensional coordinates of the foot skeleton, CT scan results are analyzed, and the center point of the calcaneus, the midpoint of the talus, and the center point of the distal end of the second metatarsal are connected to generate a reference force line in a static neutral position. Then, a high-frequency pressure sensor array synchronously records the real-time pressure distribution of the plantar contact surface during the gait cycle. Subsequently, the centroid coordinates of the plantar pressure distribution are calculated frame by frame to generate a time-continuous plantar pressure center trajectory. Next, a Savitzky-Golay filter is used to eliminate sensor noise while preserving gait characteristic waveforms. A deviation threshold between the dynamic pressure center trajectory and the reference force line is set. The offset time of the pressure center trajectory exceeding the deviation threshold within a single gait cycle is then statistically analyzed. Simultaneously, the Euclidean distance between each point on the pressure center trajectory and the reference force line is calculated. Finally, the maximum distance value among all deviation points is taken. Furthermore, the offset time, maximum offset distance, and reference force line are normalized, and weighting coefficients are set according to foot anatomical characteristics to calculate the dynamic force line offset index. Then, using a dynamic time warping algorithm, the phalangeal synergistic parameters are time-aligned with changes in ankle joint angle; the coupling strength between phalangeal pressure response and ankle joint angle is calculated to generate the foot load effect. Subsequently, the dynamic force line offset index, foot load effect, and rotational deformity angle are input into a nonlinear mapping model; furthermore, based on clinical measured data and finite element simulation data, the mean square error and clinical expert scores are used as a joint loss function; finally, a biomechanical model is output.

[0100] This approach analyzes CT scan results based on the three-dimensional coordinates of the foot skeleton to determine the baseline force line, helping to eliminate bone alignment errors caused by the absence of gravity effects in static CT images. Based on the foot force line offset, it determines the offset time and maximum offset distance of the plantar pressure center trajectory relative to the baseline force line, helping to quantify the temporal persistence and spatial severity of the foot force line offset, compensating for the inability to analyze instantaneous fluctuations in the gait cycle. A dynamic force line offset index is established based on the offset time, maximum offset distance, and baseline force line to reflect the overall severity of foot biomechanical imbalance under gravity. Analysis of phalangeal coordination parameters determines the impact of foot load, avoiding the problem of coarse load distribution strategies caused by ignoring phalangeal coordination parameters. A biomechanical model is established based on the dynamic force line offset index, the impact of foot load, and rotational deformity angle, helping to avoid interference from local deformities in global load assessment.

[0101] In some embodiments, the patient's initial condition is determined based on the patient's medical history; high-risk areas of the foot are identified based on a biomechanical model; the high-risk areas of the foot are analyzed to determine the pressure bearing potential of adjacent areas; the adjustment priority of foot load is determined based on the pressure bearing potential; and the pressure transfer path is determined based on the patient's initial condition.

[0102] The patient's initial state can be a comprehensive description of the anatomical structure and dynamic biomechanical characteristics of the patient's foot under real gravity.

[0103] High-risk areas of the foot may be pathological areas that require priority load adjustment.

[0104] Adjacent areas can be anatomical areas of the foot that are in direct contact with high-risk areas or located on the path of mechanical conduction.

[0105] Pressure bearing capacity can be a level of ability for adjacent areas to safely absorb additional loads.

[0106] Specifically, the pathological features in patient cases are spatially matched with the coordinates of high-risk areas in the biomechanical model; an initial load limitation range is set based on the pain threshold to determine the patient's initial state. Then, based on the biomechanical model, areas of significant mechanical imbalance are screened according to the dynamic force line offset index; combined with the influence of foot load, high-risk areas of the foot are identified. Subsequently, based on the linear relationship between Hounsfield units and bone mineral density, a grayscale-bone mineral density conversion formula is established through calibration experiments using grayscale values ​​from standing CT images and a standard bone mineral density calibration phantom to calculate the bone load-bearing strength of adjacent areas; then, the effective pressure contact area ratio of high-risk areas of the foot within a single gait cycle is statistically analyzed to determine the pressure-bearing potential of adjacent areas. Furthermore, based on the pressure-bearing potential, priority rules are defined: first, the higher the imbalance score of the high-risk area, the higher the priority; second, the higher the pressure-bearing potential level of adjacent areas, the higher the priority weight of the corresponding high-risk area; thus determining the adjustment priority of foot load. Finally, based on the stiffness tensor theory in continuum mechanics, a weighted graph network is constructed with the high-risk region as the starting point and the adjacent high-potential region as the ending point. The Dijkstra algorithm is used to calculate the minimum impedance path from the high-risk region to the high-potential region, i.e., the pressure transfer path.

[0107] This approach, based on patient case records, determines the patient's initial condition, helping to avoid misjudgments of non-weight-bearing areas and eliminating the disconnect between anatomical and biomechanical data caused by fragmented multimodal data. Based on biomechanical models, high-risk areas of the foot are identified, eliminating the crudeness of manually defining areas based on physician experience. Analyzing these high-risk areas determines the pressure-bearing potential of adjacent areas, preventing pressure transfer to low-bone-density areas and eliminating the problem of load distribution strategies failing to consider the potential of adjacent areas. Based on pressure-bearing potential, the priority of foot load adjustment is determined, helping to resolve the contradiction between the unbalanced decompression needs of high-risk areas and the load-bearing capacity of adjacent areas. Based on the patient's initial condition, the pressure transfer path is determined, helping to eliminate the risk of compensatory injury due to incomplete path planning.

[0108] In some embodiments, a clinical validation dataset is obtained; based on the clinical validation dataset, the mapping relationship between force line offset and actual disease severity is determined; based on the mapping relationship, an index grading table is established; when the dynamic force line offset index falls into the medium-risk range, a buffer zone marker is added to the load allocation recommendation.

[0109] Clinical validation datasets can be collections of multimodal data collected synchronously under real gravity conditions.

[0110] Force line offset can be a quantitative parameter that measures the degree of deviation of the trajectory of the center of plantar pressure relative to the reference force line.

[0111] The actual severity of the condition can be assessed using objective clinical indicators.

[0112] The mapping relationship can be a mathematical correlation model established to weight and match the standardized force line offset with the disease severity index.

[0113] The index grading table can be a classification table of mechanical compensation levels, including mild compensation range, moderate risk range, and severe injury range.

[0114] Mild compensation range can indicate that the foot soft tissue can compensate for pressure imbalance through its own deformation without the need for external forced intervention.

[0115] The moderate risk zone can indicate that the local tissues of the foot have entered a critical state of micro-damage, and the pressure gradient transfer needs to be guided by the marking of the buffer zone.

[0116] The severe injury zone can indicate that irreversible damage has occurred to the foot's biomechanical structure, requiring activation of the foot and ankle fixation module to forcibly unload the load in the high-risk area.

[0117] The buffer zone marker can be an annular semi-transparent overlay layer superimposed in the load distribution recommendation, forming a transition area extending outward from the geometric center of the high-risk area along the direction of lowest mechanical impedance.

[0118] Specifically, clinical validation datasets from high-frequency dynamic pressure sensors were collected. Then, the offset time and maximum offset distance were dimensionlessly processed to eliminate the influence of individual size differences. Subsequently, a multiple linear regression model based on well-known biomechanical techniques was used, with standardized offset time and maximum offset distance as independent variables and disease severity indicators as dependent variables, to establish a weighting equation. Then, the clinical validation dataset output by the multiple linear regression model was used to perform K-means clustering to define the mapping relationship between force line offset and actual disease severity. Furthermore, an index grading table was defined based on the mapping relationship: First, intervals requiring only local fine-tuning of load distribution were identified as mild compensation intervals; second, intervals requiring consideration of buffer zone markings and potential assessment of adjacent areas were identified as moderate-risk intervals; then, intervals triggering forced decompression rules and generating warning signals were identified as severe injury intervals. Finally, when the dynamic force line offset index falls into the moderate-risk interval, a buffer zone marking, extending outward from the direction of lowest mechanical resistance along the geometric center of the high-risk area, was superimposed on the load distribution recommendations for the high-risk area of ​​the foot.

[0119] This approach utilizes clinically validated datasets, helping to eliminate the problem of fragmented multimodal data. Based on these datasets, the mapping relationship between force line offset and actual disease severity is determined, significantly improving the analytical accuracy of load imbalance mechanisms. An index grading table is established based on this mapping relationship, helping to eliminate the problem of coarse load allocation strategies and avoiding compensatory risks caused by reliance on physician subjective experience. When the dynamic force line offset index falls into the moderate-risk range, adding a buffer zone marker to the load allocation recommendation helps address the issue of missing gravity effects and prevents compensatory damage in adjacent areas due to sudden decompression.

[0120] In some embodiments, the historical maximum pressure bearing value of adjacent areas is obtained; the real-time structural strength coefficient of adjacent areas is calculated based on a biomechanical model; the contact area ratio between adjacent areas and high-risk areas is calculated based on the three-dimensional coordinates of the foot bones; and a pressure bearing potential score is generated based on the historical maximum pressure bearing value, structural strength coefficient, and contact area ratio to determine the pressure bearing potential of adjacent areas.

[0121] The historical maximum pressure bearing value can be the maximum pressure value recorded by a high-frequency dynamic pressure sensor in an adjacent area during a complete gait cycle.

[0122] The real-time structural strength coefficient can be a dynamic parameter that characterizes the instantaneous mechanical integrity of adjacent regions under gravity load.

[0123] High-risk areas can be anatomical areas that require protective decompression or load transfer.

[0124] The contact area ratio can be the percentage of the overlapping area of ​​the contact surfaces of adjacent areas and high-risk areas in three-dimensional space, relative to the total surface area of ​​the adjacent areas.

[0125] Pressure bearing potential score can be a standardized score used for intelligent decision-making on the feasibility of adjacent areas absorbing additional pressure loads.

[0126] Specifically, high-frequency dynamic pressure sensor time-series data of the target adjacent region during past gait cycles are retrieved from the clinical validation dataset to extract pressure peaks and their corresponding time proportions. Then, the maximum pressure value within consecutive gait cycles is selected using a sliding time window algorithm and recorded as the historical maximum pressure load value. Subsequently, the standing CT three-dimensional skeletal coordinates of the adjacent region are input into a pre-trained biomechanical model to extract bone density distribution, ligament attachment point locations, and soft tissue thickness parameters. Then, based on the regional structural strength formula defined in the "Clinical Guidelines for Foot and Ankle Biomechanics" and combined with the real-time acquired dynamic pressure distribution, the elastic modulus attenuation rate in the vertical load direction within the region is calculated, and the dynamic structural strength coefficient is output. Then, based on the three-dimensional coordinates of the foot skeleton, the empty sphere criterion of the Delaunay triangulation algorithm is used to construct the geometric topological relationship of the contact surface between the adjacent region and the high-risk region. Finally, the ratio of the projected overlap area of ​​the contact surface between the adjacent region and the high-risk region to the total surface area of ​​the adjacent region is calculated to obtain the contact area ratio. Finally, the historical maximum pressure bearing capacity, real-time structural strength coefficient, and contact area ratio are normalized to establish a weighted decision matrix. Then, a pressure bearing potential score is generated by linear weighted summation. When the pressure bearing potential score is too high, it is determined to be a high-potential area that can bear additional pressure loads; when the pressure bearing potential score is too low, it is marked as a restricted bearing area.

[0127] This scheme obtains the historical maximum pressure bearing capacity of adjacent areas, avoiding the problem that static pressure plates cannot capture dynamic peak values. Real-time structural strength coefficients of adjacent areas are calculated based on a biomechanical model, which helps quantify the actual degree of load-bearing capacity degradation under gravity. The contact area ratio between adjacent and high-risk areas is calculated based on the three-dimensional coordinates of the foot bones, helping to avoid geometric distortions caused by two-dimensional projection analysis. Based on the historical maximum pressure bearing capacity, structural strength coefficients, and contact area ratios, a pressure bearing potential score is generated to determine the pressure bearing potential of adjacent areas, achieving quantification and reproducibility of potential assessment while avoiding compensatory damage.

[0128] Figure 3 This is a schematic diagram of the structure of an adaptive foot dynamic load distribution system provided in an embodiment of this application, as shown below. Figure 3 As shown, the adaptive foot dynamic load distribution system 300 of this embodiment includes: a data analysis module 301, a model building module 302, a priority and path determination module 303, and a suggestion generation module 304.

[0129] The data analysis module 301 is used to acquire foot detection data and patient cases; analyze the foot detection data, and determine the foot force line offset. The model building module 302 is used to build a biomechanical model based on the foot force line offset. The priority and path determination module 303 is used to determine the adjustment priority and pressure transfer path of foot load based on the patient's case and the biomechanical model. It is suggested that module 304 be used to generate load allocation suggestions based on the adjustment priority and the pressure transfer path.

[0130] Optionally, when the data analysis module 301 acquires foot detection data, it is used for: Obtain the current load signal, analyze the load signal, and determine the load source; Based on the patient's medical records, determine whether the load-bearing source meets the testing requirements; If the conditions are met, obtain the CT scan results and pressure data; Analyze the CT scan results to determine the three-dimensional coordinates of the foot bones and the soft tissue deformation characteristics; Analyze the pressure data to determine the dynamic pressure distribution and dynamic changes in the limbs; The three-dimensional coordinates of the foot bones, the soft tissue deformation characteristics, the dynamic pressure distribution, and the dynamic changes of the limbs are determined as the foot detection data.

[0131] Optionally, when the data analysis module 301 analyzes the pressure data to determine the dynamic pressure distribution and limb dynamic changes, it is used for: Analyze the pressure data to determine the changes in pressure distribution; Based on the changes in pressure distribution, determine whether the current stage is dynamic detection. If it is in the state, then the change in pressure distribution is determined as the dynamic pressure distribution; Based on the three-dimensional coordinates of the foot bones, the center point of the calcaneus and the center point of the metatarsal heads are marked and determined; Based on the center point of the calcaneus and the center point of the metatarsal heads, the dynamic changes of the limbs are determined according to the changes in pressure distribution.

[0132] Optionally, when the data analysis module 301 determines the dynamic changes of the limb based on the center point of the calcaneus and the center point of the metatarsal heads, according to the changes in pressure distribution, it is used for: Based on the three-dimensional coordinates of the foot bones, the forefoot region, midfoot region, and heel region are determined; Based on the changes in pressure distribution, determine the changes in the forefoot region, midfoot region, and heel region; Based on the center point of the heel bone and the center point of the metatarsal head, and according to the changes in the forefoot region, midfoot region, and heel region, the tibial torsion angle and the ankle joint inversion or eversion angle are determined. The tibial torsion angle and the ankle joint inversion or eversion angle are defined as the dynamic changes of the limb.

[0133] Optionally, when the model building module 302 builds a biomechanical model based on the foot force line offset, it is used for: Analyze the dynamic pressure distribution to determine the rotational distortion angle; Analyze the dynamic changes of the limbs to determine the phalanx coordination parameters; A biomechanical model is established based on the foot force line offset, the rotational deformity angle, and the toe bone coordination parameters.

[0134] Optionally, when the model building module 302 builds a biomechanical model based on the foot force line offset, the rotational deformity angle, and the phalanx coordination parameters, it is used for: Based on the three-dimensional coordinates of the foot bones, the CT scan results are analyzed to determine the baseline force line; Based on the foot force line offset, determine the offset time and maximum offset distance of the foot pressure center trajectory relative to the reference force line; A dynamic force line offset index is established based on the offset time, the maximum offset distance, and the reference force line; Analyze the phalangeal synergistic parameters to determine the impact of foot load; A biomechanical model is established based on the dynamic force line offset index, the foot load effect, and the rotational deformity angle.

[0135] Optionally, when the priority and path determination module 303 determines the adjustment priority and pressure transfer path of the foot load based on the patient's case and the biomechanical model, it is used for: Based on the patient's medical record, the patient's initial condition is determined; Based on the biomechanical model, high-risk areas of the foot were identified; Analyze the high-risk areas of the foot to determine the pressure bearing potential of adjacent areas; Based on the pressure bearing potential, determine the adjustment priority of foot load; Based on the patient's initial condition, a pressure transfer pathway is determined.

[0136] Optionally, the adaptive foot dynamic load distribution system further includes a marker increment module 305, used for: Obtain the clinical validation dataset; Based on the clinical validation dataset, the mapping relationship between force line offset and actual disease severity was determined; Based on the mapping relationship, an index grading table is established; the index grading table includes a mild compensation range, a moderate risk range, and a severe damage range; when the dynamic force line offset index falls into the moderate risk range, a buffer area marker is added to the load allocation recommendation.

[0137] Optionally, when the priority and path determination module 303 analyzes the high-risk area of ​​the foot and determines the pressure bearing potential of adjacent areas, it is used for: Obtain the historical maximum pressure bearing value of adjacent areas; Calculate the real-time structural strength coefficient of adjacent regions based on the biomechanical model; Based on the three-dimensional coordinates of the foot bones, the contact area ratio between adjacent areas and high-risk areas is calculated; Based on the historical maximum pressure bearing capacity, structural strength coefficient, and contact area ratio, a pressure bearing potential score is generated to determine the pressure bearing potential of adjacent areas.

[0138] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method of adaptive foot dynamic load distribution, characterized in that, The method comprises the following steps: Obtaining foot detection data and patient case; Analyzing the foot detection data to determine the foot force line offset; According to the foot force line offset, a biomechanical model is established; According to the patient case and the biomechanical model, the adjustment priority and pressure transfer path of the foot load are determined; Based on the adjustment priority, the load distribution suggestion is generated according to the pressure transfer path.

2. The method of claim 1, wherein, The foot detection data includes: Obtaining the weight signal at the current time, analyzing the weight signal to determine the weight source; According to the patient case, determine whether the weight source meets the detection requirements; If it meets, obtain the CT scan result and pressure data; Analyzing the CT scan result to determine the three-dimensional coordinates of the foot skeleton and the soft tissue deformation characteristics; Analyzing the pressure data to determine the dynamic pressure distribution and limb dynamic change; The three-dimensional coordinates of the foot skeleton, the soft tissue deformation characteristics, the dynamic pressure distribution and the limb dynamic change are determined as the foot detection data.

3. The method of claim 2, wherein, The analysis of the pressure data to determine the dynamic pressure distribution and limb dynamic change includes: Analyzing the pressure data to determine the pressure distribution change; According to the pressure distribution change, determine whether it is in the dynamic detection stage; If it is, the pressure distribution change is determined as the dynamic pressure distribution; According to the three-dimensional coordinates of the foot skeleton, the root bone center point and the metatarsal head center point are determined; Based on the root bone center point and the metatarsal head center point, the limb dynamic change is determined according to the pressure distribution change.

4. The method of claim 3, wherein, The determination of the limb dynamic change based on the root bone center point and the metatarsal head center point according to the pressure distribution change includes: According to the three-dimensional coordinates of the foot skeleton, the forefoot region, the midfoot region and the heel region are determined; According to the pressure distribution change, the forefoot region change, the midfoot region change and the heel region change are determined; Based on the root bone center point and the metatarsal head center point, the tibial torsion angle and the ankle joint inversion or eversion angle are determined according to the forefoot region change, the midfoot region change and the heel region change; The tibial torsion angle and the ankle joint inversion or eversion angle are determined as the limb dynamic change.

5. The method of claim 2, wherein, The establishment of the biomechanical model according to the foot force line offset includes: Analyzing the dynamic pressure distribution to determine the rotation deformity angle; Analyzing the limb dynamic change to determine the toe bone coordination parameter; According to the foot force line offset, the rotation deformity angle and the toe bone coordination parameter, a biomechanical model is established.

6. The method of claim 5, wherein, The establishment of the biomechanical model according to the foot force line offset, the rotation deformity angle and the toe bone coordination parameter includes: Based on the three-dimensional coordinates of the foot skeleton, the CT scan result is analyzed to determine the reference force line; According to the foot force line offset, the offset time and the maximum offset distance of the center of pressure of the foot are determined compared with the reference force line; According to the offset time, the maximum offset distance and the reference force line, a dynamic force line offset index is established; Analyzing the toe bone coordination parameter to determine the foot load influence; According to the dynamic force line deviation index, the foot load influence and the rotation deformity angle, a biomechanical model is established.

7. The method of claim 2, wherein, According to the patient case and the biomechanical model, the adjustment priority of foot load and the pressure transfer path are determined, which comprises: According to the patient case, the initial state of the patient is determined; According to the biomechanical model, the high-risk area of the foot is determined; The pressure bearing potential of the adjacent area is determined by analyzing the high-risk area of the foot; According to the pressure bearing potential, the adjustment priority of foot load is determined; According to the initial state of the patient, the pressure transfer path is determined.

8. The method of claim 6, wherein, After the dynamic force line deviation index is established according to the deviation time, the maximum deviation distance and the reference force line, it further comprises: Obtain a clinical verification data set; According to the clinical verification data set, the mapping relationship between the force line deviation and the actual disease severity is determined; According to the mapping relationship, an index classification table is established; the index classification table includes a mild compensation interval, a moderate risk interval and a severe damage interval; when the dynamic force line deviation index falls into the moderate risk interval, a buffer area mark is added in the load distribution suggestion.

9. The method of claim 7, wherein, The pressure bearing potential of the adjacent area is determined by analyzing the high-risk area of the foot, which comprises: Obtain the historical maximum pressure bearing value of the adjacent area; According to the biomechanical model, the real-time structure strength coefficient of the adjacent area is calculated; Based on the three-dimensional coordinates of the foot skeleton, the contact area ratio of the adjacent area and the high-risk area is calculated; According to the historical maximum pressure bearing value, the structure strength coefficient and the contact area ratio, the pressure bearing potential score is generated to determine the pressure bearing potential of the adjacent area.

10. An adaptive foot dynamic load distribution system characterized by, Applied to the method of any one of claims 1-9, comprising: A data analysis module is used to obtain foot detection data and patient cases; the foot detection data is analyzed to determine the foot force line deviation; A model establishment module is used to establish a biomechanical model according to the foot force line deviation; A priority and path determination module is used to determine the adjustment priority of foot load and the pressure transfer path according to the patient case and the biomechanical model; A suggestion generation module is used to generate a load distribution suggestion based on the adjustment priority according to the pressure transfer path.

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