Analysis method and system for determining counterforce characteristics based on six-dimensional force sensor array

Abstract

The invention discloses an analysis method and system for determining counterforce characteristics based on a six-dimensional force sensor array. The method comprises the following steps: S1, constructing a six-dimensional force sensor array platform by adopting a modular matrix layout; s2, selecting a subject to execute a target action on the six-dimensional force sensor array platform to trigger each six-dimensional force sensor; s3, directly mapping the three-dimensional force components (Fx, Fy and Fz) into three-dimensional space coordinate points and torque components (Mx, My and Mz), and storing the three-dimensional space coordinate points and the torque components (Mx, My and Mz) in a database; s4, respectively drawing a beta-t curve, an Fz '-Ft phase diagram and a CoP trajectory diagram, and carrying out fast Fourier transform on the six-dimensional reaction force data; meanwhile, the system generates a visual image of stress data by each six-dimensional force sensor according to the number of the deployed six-dimensional force sensors. According to the method, the multi-sensor six-dimensional force data can be collected and processed at high frequency, a visual three-dimensional visualization result is generated, and multi-dimensional mechanical characteristics are extracted.

Description

Technical Field

[0001] This invention relates to the field of biomechanical measurement and human-computer interaction technology, specifically to an analysis method and system for determining reaction force characteristics based on a six-dimensional force sensor array. Background Technology

[0002] In the fields of biomechanical measurement and human-computer interaction, accurately acquiring motion biomechanical characteristics is crucial for understanding motion mechanisms, assisting rehabilitation therapy, and optimizing robot motion control. Currently, traditional force plate systems mostly employ three-dimensional force sensors (Fx, Fy, Fz) or two-dimensional pressure distribution measurements. However, these current methods have the following drawbacks: 1) Missing force information leads to analysis bias: Traditional three-dimensional force sensors can only measure force components in three directions and cannot simultaneously acquire torque components (Mx, My, Mz). During human movement, torque plays a key role in analyzing the force state of joints, movement stability and energy transfer. Missing torque information will lead to errors in the calculation of the point of application, which in turn affects the accurate understanding of the mechanism of motion mechanics. 2) Insufficient spatial resolution: Single-point measurement is difficult to characterize the spatial distribution characteristics of force in dynamic motion; human movement is a complex multi-joint coordinated process, and the magnitude and direction of the force borne by different parts of the body during movement are different, and single-point measurement cannot capture these subtle changes. 3) Lack of in-depth mechanical feature mining: Existing technology lacks quantitative analysis of the shape and curvature of force vector surfaces, and cannot reveal the deep laws of kinematics; force vector surfaces can intuitively show the distribution of force in space, and the curvature changes can reflect the uniformity and stability of force distribution. 4) Limited application scenarios: In the field of humanoid robot motion training, the force information provided by traditional measurement methods is insufficient, which cannot provide enough feedback for the robot's motion control algorithm, making it difficult to achieve accurate motion simulation and optimization; in terms of quantitative assessment of movement disorders, the inability to fully obtain human biomechanical characteristics leads to inaccurate assessment results, affecting the formulation of personalized rehabilitation plans. Summary of the Invention

[0003] To address the aforementioned problems, the present invention aims to propose an analytical method and system for determining reaction force characteristics based on a six-dimensional force sensor array. This method can synchronously acquire and process multi-sensor six-dimensional force data in real time at high frequency, generate intuitive three-dimensional visualization results, and extract multi-dimensional mechanical features. This method can accurately characterize the gait features of subjects and can be applied to kinematic research in healthy individuals and pathological gait monitoring, showing broad application prospects.

[0004] This was achieved through the following technical solutions: Firstly, an analytical method for determining reaction force characteristics based on a six-dimensional force sensor array is proposed. This method includes the following steps: S1. Constructing a six-dimensional force sensor array platform, which adopts a modular matrix layout, arranging multiple independent six-dimensional force sensors to cover the entire contact area of ​​the human foot. Each six-dimensional force sensor connects to a local area network and a central controller via a corresponding communication protocol, transmitting real-time acquired data frames to the central controller. The data frames include: a timestamp, the number of each six-dimensional force sensor, and six-dimensional reaction force data. S2. Selecting a subject to perform a target action on the six-dimensional force sensor array platform. The platform triggers each six-dimensional force sensor according to the action initiation signal, and each sensor begins data acquisition. Simultaneously, a corresponding filtering algorithm is used to preprocess the six-dimensional reaction force data acquired by each sensor. S3. After preprocessing, the three-dimensional force components (Fx, Fy, Fz) in the six-dimensional reaction force data acquired by each sensor at time t are directly mapped to a three-dimensional spatial coordinate point Point. sensor = (Fx, Fy, Fz), where t is the timestamp, and the sensor is the number for each six-dimensional force sensor. Simultaneously, the torque components (Mx, My, Mz) in the six-dimensional reaction force data are stored in the database for calculating the actual point of application of each motion reaction force. The reaction force at each force point is plotted to form a continuous three-dimensional force surface. Then, the projection of each surface onto the XY, XZ, and YZ planes is drawn, and each surface is dynamically rendered. S4: The six-dimensional reaction force data collected by all six-dimensional force sensors are grouped according to left and right feet, and the six-dimensional reaction force data are fused according to left / right foot groups. Based on the fused six-dimensional reaction force data, the β-t curve of the angle β between the total resultant force F and the XOY plane as a function of time, and the horizontal resultant force F on the XOY plane are plotted. t With the vertical component Fz ’ Fz ’ -F t Phase diagram; then, a fast Fourier transform is performed on the fused six-dimensional reaction force data to generate the corresponding spectrum diagram and calculate the power spectral density, and the CoP trajectory diagram of the pressure center coordinates of each six-dimensional force sensor is plotted. Simultaneously, based on the fused vertical component force Fz... ’ The threshold is used to divide the subject's gait into the support phase and the swing phase, and the overlap rate of the subject's two feet is calculated; S5, a software interface based on PyQt5 is developed, which automatically generates a visualized image of the stress data of each six-dimensional force sensor according to the number of six-dimensional force sensors deployed on the six-dimensional force sensor array platform, and displays it in an orderly split-screen manner. This invention can synchronously acquire and process six-dimensional force data from multiple sensors at high frequency, generate intuitive three-dimensional visualization results, and extract multi-dimensional mechanical features.

[0005] Preferably, in step S1, the full contact area of ​​the human foot includes at least the forefoot, heel, medial side, and lateral side. Through the arrangement of six-dimensional force sensors, the dynamic changes of three-dimensional forces and torques during human movement can be captured in real time, improving data reliability and providing high-precision, full-dimensional raw data support for subsequent mechanical analysis.

[0006] Preferably, in step S1, each six-dimensional force sensor has a built-in communication module and is configured with a fixed IP address and port number. By configuring a fixed IP address and port number for the six-dimensional force sensor, data acquisition synchronization can be ensured.

[0007] Preferably, in step S2, when preprocessing the six-dimensional reaction force data using the corresponding filtering algorithm, the subject's gait swing period is first identified based on the vertical component force Fz, and the six-dimensional reaction force data segments within the gait swing period are extracted as noise samples; then, a fast Fourier transform is performed on the noise samples and their power spectral density is calculated to obtain the frequency range of noise energy concentration, denoted as . After excluding the noise frequency range, the remaining frequency range that is the inherent frequency range of the effective signal is denoted as . By employing a bandpass filtering algorithm to preprocess the six-dimensional reaction force data, it is possible to suppress high-frequency noise in the data while preserving the transient characteristics of the force signal.

[0008] Preferably, in step S4, the total resultant force F is calculated based on the square root of the squares of each component force: The angle β of the forces is based on the vertical component Fz. ’ Horizontal resultant force F t Calculate the ratio: , where Fx ’ 、Fy ’ and Fz ’ These represent the components of the fused six-dimensional reaction force data in the horizontal x-direction, horizontal y-direction, and vertical direction. By plotting the β-t curve, the temporal variation of the force vector tilt angle can be analyzed.

[0009] Preferably, in step S4, the horizontal resultant force F t According to Fx ’ and Fy ’ Calculate by square and square root: By drawing Fz ’ -Ft phase diagrams can assess the force balance characteristics during human movement.

[0010] Preferably, in step S4, the formula for performing the Fast Fourier Transform is: Where X[k] is the output discrete spectrum sequence, k is the frequency point, x[n] is the fused six-dimensional force component of length N, n is the sequence number, n=0,1,…,N, and the imaginary unit. , The rotation factor is used. By performing a fast Fourier transform on each of the six-dimensional force components, the energy distribution of the force signal at different frequencies can be analyzed, and the motion stability and muscle force exertion patterns can be further identified.

[0011] Preferably, in step S4, the formula for calculating the coordinates of the pressure center is: , Where Z0 = 0.0109, is the distance from the force-bearing surface to the design center plane of the force sensor. Calculating the trajectory of the pressure center helps to observe whether its shape is continuous and symmetrical, and quantifies the trajectory range and distribution characteristics, which is used to evaluate the center of gravity control capability.

[0012] Preferably, in step S4, during the subject's movement, the six-dimensional force sensor data of the subject's left and right feet are compared simultaneously to calculate the subject's bipedal overlap rate. The calculation formula is: overlap rate = (time of simultaneous force application on both feet / total gait cycle time) * 100%. By calculating the subject's bipedal overlap rate, the overlap stage of the six-dimensional force during bipedal movement can be accurately determined, and the coordination efficiency of alternating bipedal movement can be quantitatively analyzed.

[0013] Secondly, this paper proposes an analysis system for determining reaction force characteristics based on a six-dimensional force sensor array. The system utilizes a PyQt5-based software interface to automatically generate and display visualized images of stress data from each six-dimensional force sensor, arranged in a sequential, split-screen manner, based on the number of six-dimensional force sensors deployed on the platform. The interface also includes: precise time positioning, allowing users to drag and position the three-dimensional surface at any time point via an interactive progress bar, while simultaneously viewing the six-dimensional force curve at that moment; localized focused analysis, enabling zooming and full-screen display of any six-dimensional force sensor view to focus on the mechanical characteristics of localized stress areas; and result export, supporting the export of three-dimensional force surface animations, β-t curves, and Fz values. ’ The analysis results of the Ft phase diagram, CoP trajectory diagram, and spectrum diagram are exported in a universal format. This invention allows for simultaneous viewing of the six-dimensional force curve at any given time, facilitating subsequent research and report generation.

[0014] The beneficial effects of this invention compared to the prior art are: The technical solution of this invention, through a six-dimensional force sensor array, synchronously acquires three-dimensional forces (Fx, Fy, Fz) and three-dimensional moments (Mx, My, Mz), which can fill the gaps in moment information, improve data reliability, effectively avoid measurement errors caused by single-point failures, and provide high-precision, full-dimensional raw data support for subsequent mechanical analysis; and this invention utilizes three-dimensional surface modeling, β-t curves, and Fz... ’Using methods such as Ft phase diagrams and frequency domain analysis, mechanical characteristics can be quantitatively characterized, enabling in-depth quantitative analysis and precise quantitative evaluation of kinematic mechanical features. Simultaneously, this invention can synchronously acquire and process six-dimensional force data from multiple sensors in real time at high frequencies, generating intuitive three-dimensional visualization results and extracting multi-dimensional mechanical features. It can also accurately characterize the gait features of subjects, making it applicable to kinematic research in healthy individuals and pathological gait monitoring, with broad application prospects. Attached Figure Description

[0015] Figure 1 This is a flowchart of an analytical method for determining reaction force characteristics based on a six-dimensional force sensor array. Detailed Implementation

[0016] The following will refer to the appendices in the embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention will be described in detail below.

[0017] like Figure 1 The diagram shows a flowchart of an analytical method for determining reaction force characteristics based on a six-dimensional force sensor array. First, a six-dimensional force sensor array platform is constructed using a modular matrix layout. Then, a subject performs a target action on the platform, triggering each of the six-dimensional force sensors. Next, the collected three-dimensional force components (Fx, Fy, Fz) are directly mapped to three-dimensional spatial coordinates and torque components (Mx, My, Mz) and stored in a database. Simultaneously, β-t curves and Fz curves are plotted. ’ - The invention generates an Ft phase diagram, a CoP trajectory diagram, and performs a fast Fourier transform on the fused six-dimensional reaction force data. Finally, based on the number of deployed six-dimensional force sensors, it generates a visualization image of the stress data for each six-dimensional force sensor. CoP stands for Center of Pressure. This invention can synchronously acquire and process six-dimensional force data from multiple sensors at high frequency, generate intuitive three-dimensional visualization results, and extract multi-dimensional mechanical features.

[0018] The method specifically includes the following steps: S1. Construct a six-dimensional force sensor array platform, employing a modular matrix layout. Twenty independent, high-precision six-dimensional force sensors are tightly arranged, covering the entire contact area of ​​the human foot. This area includes at least the forefoot, heel, medial side, and lateral side. This array design enables real-time capture of the dynamic changes in three-dimensional forces and moments during human movement, improving data reliability and providing high-precision, full-dimensional raw data support for subsequent mechanical analysis. Each six-dimensional force sensor connects to a local area network via UDP or CAN protocols and uses a standardized interface to connect to the central controller, transmitting real-time acquired data frames to the central controller. The sampling frequency can reach up to 1000Hz. A custom data frame format is used, including a timestamp, the number of each six-dimensional force sensor, and six-dimensional reaction force data. A verification mechanism ensures reliable data transmission. Each six-dimensional force sensor also has a built-in communication module, such as a UDP communication module, configured with a fixed IP address and port number to ensure synchronized data acquisition. UDP stands for User Datagram. Protocol, or User Datagram Protocol, is used to provide data transmission services; CAN stands for Controller Area Network, a serial communication bus standard and protocol used in real-time control systems.

[0019] S2. Select a healthy adult volunteer as the subject to perform the target action on the six-dimensional force sensor array platform. The six-dimensional force sensor array platform will trigger each six-dimensional force sensor according to the action start signal. Each six-dimensional force sensor will start data acquisition. At the same time, the six-dimensional force sensor array platform will synchronously record the six-dimensional reaction force data and corresponding timestamps collected by each six-dimensional force sensor to ensure the consistency of the time dimension of the collected data. The platform will also use the corresponding filtering algorithm to preprocess the six-dimensional reaction force data collected by each six-dimensional force sensor.

[0020] In this embodiment, in step S2, when the present invention uses a bandpass filtering algorithm to preprocess the six-dimensional reaction force data, it first identifies the subject's gait swing period based on the vertical component force Fz, and extracts the six-dimensional reaction force data fragments within the gait swing period as noise samples; then, it performs a fast Fourier transform on the noise samples and calculates their power spectral density, obtaining the frequency range of noise energy concentration, denoted as... After excluding the noise frequency range, the remaining frequency range that is the inherent frequency range of the effective signal is denoted as . Among them, the gait swing period refers to the time between two steps when the sole of the subject's foot leaves the six-dimensional force sensor array platform. The method of the present invention uses a bandpass filtering algorithm to preprocess the six-dimensional reaction force data, which can suppress high-frequency noise in the data while retaining the transient characteristics of the force signal.

[0021] S3. After preprocessing, the six-dimensional reaction force data collected by each six-dimensional force sensor are first integrated according to the time series. Then, the three-dimensional force components (Fx, Fy, Fz) in the six-dimensional reaction force data collected by each six-dimensional force sensor at time t are directly mapped to three-dimensional spatial coordinate points Point. sensor = (Fx, Fy, Fz), and simultaneously store the torque components (Mx, My, Mz) from the six-dimensional reaction force data in the database for calculating the actual point of application of each motion reaction force. Here, t is the timestamp, sensor is the number of each six-dimensional force sensor, Mx is the torque of rotation about the horizontal x-direction, My is the torque of rotation about the horizontal y-direction, and Mz is the torque of rotation about the vertical direction. Then, the reaction force at each force point is drawn, forming a continuous three-dimensional force surface. The projections of each surface onto the XY, XZ, and YZ planes are drawn, and the spatial distribution of the force magnitude is visually presented through color mapping. The greater the force, the larger its spatial distribution. The force component data of the corresponding plane is extracted synchronously from the projection map, realizing real-time correlation between the three-dimensional surface and the two-dimensional projection, facilitating the observation of the distribution pattern of force vectors in different dimensions. Simultaneously, the OpenGL engine is used to dynamically render each surface, achieving smooth rendering of the three-dimensional force surface changing over time, forming an animated effect that visually demonstrates the dynamic evolution of the force data. OpenGL stands for Open Graphics. Library, or Open Graphics Library, is used by the OpenGL engine to handle the dynamic rendering of surfaces.

[0022] S4. Group the six-dimensional reaction force data collected by all six-dimensional force sensors according to left and right feet, and fuse the six-dimensional reaction force data according to left / right feet groups. Based on the fused six-dimensional reaction force data, plot the β-t curve of the angle β between the total resultant force F and the XOY plane as a function of time, and plot the horizontal resultant force F in the XOY plane. t With the vertical component Fz ’ Fz ’ -F t Phase diagram; then, a fast Fourier transform is performed on the fused six-dimensional reaction force data to generate the corresponding spectrum diagram and calculate the power spectral density, and the CoP trajectory diagram of the pressure center coordinates of each six-dimensional force sensor is plotted. Simultaneously, based on the fused vertical component force Fz... ’ The threshold is used to divide the subject's gait into the support phase and the swing phase, and the overlap rate of the subject's two feet is calculated.

[0023] Specifically, when any perpendicular component force Fz ’ When the value exceeds the threshold and continues to rise, it is determined to be the starting point of the support phase, when the vertical component force Fz ’When the gait gradually falls below a threshold, it is determined to be the end of the support phase. The time from the start to the end of the support phase is the support phase, and the remaining time is the swing phase. During the subject's movement, the six-dimensional force sensor data of the subject's left and right feet are compared simultaneously to calculate the subject's bipedal overlap rate. The calculation formula is: overlap rate = (time of simultaneous force application to both feet / total gait cycle time) * 100%. Among them, the support phase refers to the vertical component of force Fz when one foot of the subject falls and lifts off the ground. ’ The swing phase, which is the period exceeding the threshold, refers to the vertical component of force Fz when one of the subject's feet is raised and lowered. ’ For periods less than a threshold, the time during which both feet are simultaneously under force refers to the time when one foot has not yet fully lifted while the other foot has already landed. The total gait cycle time refers to the time from when one foot lands to when it lifts and then lands again. This invention utilizes the vertical component force Fz. ’ The threshold-based intelligent segmentation of gait cycles can accurately locate the contact and airborne phases of the foot with the ground within each gait cycle. By calculating the overlap rate of the subject's two feet, it can accurately determine the overlap phase of the six-dimensional forces during bipedal movement and quantify the collaborative efficiency of bipedal alternating movement.

[0024] In this embodiment, in step S4, the total resultant force F is calculated based on the square root of the squares of each component force: The angle β of the forces is based on the vertical component Fz. ’ Horizontal resultant force F t Calculate the ratio: , where Fx ’ 、Fy ’ and Fz ’ These are the components of the fused six-dimensional reaction force data in the horizontal x-direction, horizontal y-direction, and vertical direction. By plotting the β-t curve, the temporal variation of the force vector tilt angle can be analyzed.

[0025] Horizontal resultant force F t According to Fx ’ and Fy ’ Calculate by square and square root: By drawing Fz ’ - The Ft phase diagram can assess the force balance characteristics during human movement, when the Fz values ​​of the left and right feet of an athlete on a six-dimensional force sensor array platform are measured. ’ The higher the symmetry of the -Ft phase diagram, the better the balance characteristics of the athlete during exercise.

[0026] The formula for performing a fast Fourier transform on the fused six-dimensional reaction force data is as follows: Where X[k] is the output discrete spectrum sequence, k is the frequency point, x[n] is the fused six-dimensional force component of length N, n is the sequence number, n=0,1,…,N, and the imaginary unit. , Let be the rotation factor; based on the calculated discrete spectrum sequences of each of the six-dimensional force components after fusion of the left and right feet, calculate the power spectral density of each discrete spectrum sequence, using the following formula: Sxx(ƒ[k]) is the power spectral density value at the corresponding frequency ƒ[k], |X[k]| 2 It is the square of the discrete spectrum sequence of any component, representing the power at that frequency point. The denominator N*ƒs is the normalization factor, where N is the number of sampling points and ƒs is the sampling frequency, used to ensure normalization. By performing a Fast Fourier Transform on each of the six-dimensional force components, the energy distribution of the force signal at different frequencies can be analyzed, and motion stability and muscle force exertion patterns can be further identified.

[0027] The formula for calculating the coordinates of the pressure center is: , Where Z0=0.0109, is the distance from the force-bearing surface to the design center surface of the force sensor. By calculating the trajectory of the pressure center, it is helpful to observe whether its shape is continuous and symmetrical, and to quantify the trajectory range and distribution characteristics, which is used to evaluate the athlete's center of gravity control ability. Among them, the continuity of the CoP trajectory indicates that the athlete has fewer interruptions and no sudden jumps during the exercise. The symmetry of the CoP trajectory indicates that the athlete has reduced the load difference between front, back, left and right during the exercise. The decrease in the x-axis or y-axis movement of the CoP trajectory indicates that the athlete's movement is smooth during the exercise. The reduction in the area enclosed by the CoP trajectory indicates that the athlete's concentration is improved during the exercise.

[0028] In this embodiment, a software interface developed using PyQt5 is automatically generated based on the number of six-dimensional force sensors deployed on the six-dimensional force sensor array platform. The interface is then displayed in an orderly, split-screen manner, generating a visualized image of the stress data for each six-dimensional force sensor. The interface also includes the following functions: precise time positioning, allowing users to drag and position the three-dimensional surface at any time point via an interactive progress bar, while simultaneously viewing the six-dimensional force curve at that moment; localized focused analysis, allowing users to zoom in and display the view of any six-dimensional force sensor in full screen, focusing on the mechanical characteristics of the localized stress area; and result export, supporting the export of three-dimensional force surface animations, β-t curves, and Fz curves. ’ - Analysis results such as Ft phase diagrams, CoP trajectory diagrams, and spectrograms are exported in a common format to facilitate subsequent research and report generation; PyQt5 is a third-party Python library for creating graphical user interface applications.

[0029] In summary, this invention, through the simultaneous acquisition of three-dimensional forces (Fx, Fy, Fz) and three-dimensional moments (Mx, My, Mz) using a six-dimensional force sensor array, can fill the gaps in moment information, improve data reliability, effectively avoid measurement errors caused by single-point failures, and provide high-precision, multi-dimensional raw data support for subsequent mechanical analysis. Furthermore, this invention utilizes three-dimensional surface modeling, β-t curves, and Fz... ’ Using methods such as Ft phase diagrams and frequency domain analysis, mechanical characteristics can be quantitatively characterized, enabling in-depth quantitative analysis and precise quantitative evaluation of kinematic mechanical features. Simultaneously, this invention can synchronously acquire and process six-dimensional force data from multiple sensors in real time at high frequencies, generating intuitive three-dimensional visualization results and extracting multi-dimensional mechanical features. It can also accurately characterize the gait features of subjects, making it applicable to kinematic research in healthy individuals and pathological gait monitoring. It has broad application prospects and significant advancements.

[0030] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. An analysis method for determining a counterforce profile based on an array of six-dimensional force sensors, characterized in that, Includes the following steps: S1. Construct a six-dimensional force sensor array platform, which adopts a modular matrix layout to arrange multiple independent six-dimensional force sensors to cover the entire contact area of ​​the human foot; each six-dimensional force sensor is connected to the local area network and the central controller through the corresponding communication protocol, and transmits the real-time collected data frames to the central controller. The data frames include: timestamp, number of each six-dimensional force sensor and six-dimensional reaction force data; S2. Select a subject to perform the target action on the six-dimensional force sensor array platform. The six-dimensional force sensor array platform will trigger each six-dimensional force sensor according to the action start signal. Each six-dimensional force sensor will start to collect data. At the same time, the corresponding filtering algorithm will be used to preprocess the six-dimensional reaction force data collected by each six-dimensional force sensor. S3. After preprocessing, the three-dimensional force components (Fx, Fy, Fz) in the six-dimensional reaction force data collected by each six-dimensional force sensor at time t are directly mapped to three-dimensional spatial coordinate points Point. sensor = (Fx, Fy, Fz), where t is the timestamp, sensor is the number of each six-dimensional force sensor, and the torque components (Mx, My, Mz) in the six-dimensional reaction force data are stored in the database to calculate the actual point of application of each motion reaction force, and the reaction force at each point of force is drawn to form a continuous three-dimensional force surface. Then, the projection of each surface on the XY, XZ and YZ planes is drawn, and each surface is dynamically rendered. S4. Group the six-dimensional reaction force data collected by all six-dimensional force sensors according to left and right feet, and fuse the six-dimensional reaction force data according to left / right feet groups. Based on the fused six-dimensional reaction force data, plot the β-t curve of the angle β between the total resultant force F and the XOY plane as a function of time, and plot the horizontal resultant force F in the XOY plane. t With the vertical component Fz ’ Fz ’ -F t Phase diagram; then, a fast Fourier transform is performed on the fused six-dimensional reaction force data to generate the corresponding spectrum diagram and calculate the power spectral density, and the CoP trajectory diagram of the pressure center coordinates of each six-dimensional force sensor is plotted. Simultaneously, based on the fused vertical component force Fz... ’ The threshold is used to divide the subject's gait into the support phase and the swing phase, and the overlap rate of the subject's two feet is calculated.

2. The analysis method for determining the characteristic of counterforce based on the array of six-dimensional force sensors according to claim 1, characterized in that, In step S1, the full contact area of ​​the human foot includes at least the forefoot, heel, medial side, and lateral side.

3. The analytical method for determining reaction force characteristics based on a six-dimensional force sensor array according to claim 1, characterized in that, In step S1, each six-dimensional force sensor has a built-in communication module and is configured with a fixed IP address and port number.

4. The analytical method for determining reaction force characteristics based on a six-dimensional force sensor array according to claim 1, characterized in that, In step S2, when preprocessing the six-dimensional reaction force data using the corresponding filtering algorithm, the gait swing period of the subject is first identified based on the vertical component force Fz, and the six-dimensional reaction force data segments within the gait swing period are extracted as noise samples; then, a fast Fourier transform is performed on the noise samples and their power spectral density is calculated to obtain the frequency range of noise energy concentration, denoted as . After excluding the noise frequency range, the remaining frequency range that is the inherent frequency range of the effective signal is denoted as . .

5. The analytical method for determining reaction force characteristics based on a six-dimensional force sensor array according to claim 1, characterized in that, In step S4, the total resultant force F is calculated based on the square root of the squares of each component force: The angle β of the forces is based on the vertical component Fz. ’ Horizontal resultant force F t Calculate the ratio: , where Fx ’ 、Fy ’ and Fz ’ These are the components of the fused six-dimensional reaction force data in the horizontal x-direction, horizontal y-direction, and vertical direction, respectively.

6. The analysis method for determining the characteristic of counterforce based on the array of six-dimensional force sensors according to claim 1, characterized in that, In step S4, the horizontal resultant force F t According to Fx ’ and Fy ’ The square root of the sum of the squares is calculated: .

7. The method of claim 1, wherein, In step S4, the formula for performing the Fast Fourier Transform is: Where X[k] is the output discrete spectrum sequence, k is the frequency point, x[n] is the fused six-dimensional force component of length N, n is the sequence number, n=0,1,…,N, and the imaginary unit. , is the rotation factor.

8. The analysis method for determining the characteristic of counterforce based on the array of six-dimensional force sensors according to claim 1, characterized in that, In step S4, the formula for calculating the coordinates of the pressure center is: , , where Z0=0.0109, is the distance from the force-bearing surface to the design center surface of the force sensor.

9. The method of claim 1, wherein, In step S4, during the subject's movement, the six-dimensional force sensor data of the subject's left and right feet are compared simultaneously to calculate the subject's double foot overlap rate. The calculation formula is: overlap rate = (time when both feet are under force simultaneously / total gait cycle time) * 100%.

10. An analysis system for determining reaction force characteristics based on a six-dimensional force sensor array, employing the analysis method for determining reaction force characteristics based on a six-dimensional force sensor array as described in any one of claims 1-9, characterized in that, The software interface, developed using PyQt5, automatically generates and displays visualized images of stress data from each of the six-dimensional force sensors deployed on the six-dimensional force sensor array platform. These images are then arranged in a sequential, split-screen format. The interface also includes: precise time positioning (allowing users to drag and position the 3D surface at any time point via an interactive progress bar, while simultaneously viewing the corresponding six-dimensional force curve); localized focused analysis (allowing users to zoom in and display the view of any six-dimensional force sensor in full screen, focusing on the mechanical characteristics of localized stress areas); and result export (supporting the export of 3D force surface animations, β-t curves, and Fz values). ’ The analysis results of the -Ft phase diagram, CoP trajectory diagram, and spectrogram are exported in a universal format.

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