Smart sports campus digital interactive teaching demonstration and feedback method

Abstract

This invention relates to the fields of smart sports and digital teaching technology, specifically a smart sports campus digital interactive teaching demonstration and feedback method. It includes a data acquisition and coupling step, acquiring motion visual images and field mechanical response data, and determining a multimodal motion interaction feature set through spatiotemporal alignment; an adaptive compensation step, based on a material property adaptive compensation model, eliminating nonlinear interference from the physical properties of the field materials on the mechanical data, and determining a true biomechanical feature vector; and a semantic mapping feedback step, inputting the feature vector into a mechanics-teaching semantic mapping network to determine a target teaching feedback instruction containing correction suggestions, driving the on-site feedback terminal to execute the demonstration operation. This invention eliminates environmental interference through a physical principle model, restoring the true biomechanical state, and solving the pain point in traditional teaching where only the form of the movement is known but not the essence of the force exertion.

Description

Technical Field

[0001] This invention relates to the fields of smart sports and digital teaching technology, specifically to a method for digital interactive teaching demonstration and feedback in smart sports campuses. Background Technology

[0002] In the current smart sports campus teaching environment, motion vision acquisition devices and field sensor arrays generate a large number of students' motion images and field mechanical response data in real time. These data have multimodal heterogeneous characteristics and are significantly affected by environmental factors.

[0003] To guide physical movements, existing solutions generally adopt a single visual recognition architecture, which extracts limb contours and posture features through image analysis. Although this approach has some capability in assessing explicit movement patterns, it cannot acquire implicit biomechanical features. Furthermore, the physical properties of the field materials, such as viscoelasticity, temperature, and aging, can cause nonlinear interference to the mechanical signals. This leads to problems such as the spatiotemporal misalignment of visual and mechanical data and the difficulty in removing material noise when processing multimodal data. Consequently, teaching evaluations become highly subjective, force characteristic analysis is distorted, and feedback delays are high, making it difficult to support accurate movement quality correction and a scientific teaching loop.

[0004] Therefore, how to eliminate physical interference from the field environment and achieve accurate coupling and semantic mapping of visual and mechanical multimodal data to improve the objectivity and real-time nature of physical education teaching feedback has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for digital interactive teaching demonstration and feedback in smart sports campuses. Specifically, the technical solution of this invention includes:

[0006] Acquire real-time motion visual image data of the target moving object and field mechanical response data collected by the field sensor array;

[0007] Spatiotemporal alignment and coupling processing is performed on motion visual image data and field mechanical response data to determine a multimodal motion interaction feature set, which includes temporally synchronized posture kinematic features and ground reaction force dynamic features.

[0008] Based on the pre-built material property adaptive compensation model, the multimodal motion interaction feature set is denoised and corrected. The material property adaptive compensation model is constructed based on the dynamic thermomechanical analysis and impact rebound test of the runway material sample to determine the real biomechanical feature vector. The material property adaptive compensation model is configured to eliminate the nonlinear interference of the physical properties of the site material on the mechanical data.

[0009] The real biomechanical feature vectors are input into a pre-trained mechanics-instructional semantic mapping network to determine the target instructional feedback instructions, which include semantic suggestions for corrective action on the quality of movement.

[0010] Based on the target teaching feedback instructions, digital interactive feedback signals are generated and driven on-site feedback terminals to perform demonstration operations.

[0011] Preferably, the motion visual image data and the field mechanical response data are spatiotemporally aligned and coupled to determine a multimodal motion interaction feature set, including:

[0012] Extracting the temporal coordinates of key skeleton points from motion vision image data;

[0013] Extract the time-series data of pressure waveforms from the site mechanical response data;

[0014] Based on a unified timestamp benchmark, the temporal coordinates of key skeletal points are mapped to the temporal data of pressure waveforms at the frame level to determine the set of multimodal motion interaction features.

[0015] Preferably, based on a pre-built adaptive compensation model for material properties, the multimodal motion interaction feature set is denoised and corrected to determine the true biomechanical feature vector, including:

[0016] Obtain current site environmental parameters, including site temperature data collected by temperature sensors and material aging index read from the maintenance database;

[0017] Based on the preset environmental parameter-mechanical coefficient mapping table, the dynamic correction factors for material damping coefficient and rebound coefficient are determined by querying and matching the current site environmental parameters.

[0018] By using dynamic correction factors, weighted compensation calculations are performed on the ground reaction force dynamics characteristics in the multimodal motion interaction feature set to determine the true biomechanical feature vector.

[0019] Preferably, the real biomechanical feature vectors are input into a pre-defined mechanics-instructional semantic mapping network to determine the target instructional feedback instructions, including:

[0020] The core biomechanical feature vectors are determined by performing feature dimensionality reduction on the real biomechanical feature vectors.

[0021] Calculate the feature Euclidean distance between the core mechanical feature vector and the preset standard motion mechanical template;

[0022] The initial feedback semantics are determined based on the feature Euclidean distance and the preset semantic mapping rule table;

[0023] The initial feedback semantics are encapsulated in natural language to determine the target teaching feedback instructions.

[0024] Preferably, the initial feedback semantics are determined based on the feature Euclidean distance and a preset semantic mapping rule table, including:

[0025] Determine whether the Euclidean distance of the feature is less than a preset compliance threshold;

[0026] If the feature Euclidean distance is less than the compliance threshold, the initial feedback semantics are determined to be an affirmative reinforcement instruction;

[0027] If the feature Euclidean distance is greater than or equal to the compliance threshold, identify the dimension with the largest absolute value of numerical deviation from the standard motion mechanics template in the core mechanical feature vector, and determine the initial feedback semantics as corrective guidance instructions based on the dimension.

[0028] Preferably, the method further includes:

[0029] The probability confidence score of the output of the mechanics-teaching semantic mapping network is used as the confidence score of the mechanics-teaching semantic mapping.

[0030] Determine whether the confidence level of the mechanics-teaching semantic mapping meets the preset confidence interval requirements;

[0031] If the conditions are not met, an auxiliary calibration process is triggered to generate a calibration prompt signal to obtain manual correction input, and the weight parameters of the mechanics-teaching semantic mapping network are adjusted according to the manual correction input.

[0032] Preferably, based on the target teaching feedback instructions, a digital interactive feedback signal is generated and the on-site feedback terminal is driven to perform demonstration operations, including:

[0033] Analyze the target teaching feedback instructions to determine the feedback modality type, which includes visual projection modality and tactile vibration modality;

[0034] If the feedback modality type is visual projection modality, a dynamic light and shadow signal containing the action correction trajectory is generated based on the action correction data in the target teaching feedback instruction and projected onto the field surface.

[0035] If the feedback mode type is tactile vibration mode, a pulse signal of a specific frequency is generated, which drives the actuator built into the field to produce physical feedback.

[0036] Preferably, the monitoring of the ground contact feedback synchronization delay is from the generation of field mechanical response data when the target moving object touches the field to the execution of the demonstration operation by the on-site feedback terminal;

[0037] Based on the ground contact feedback synchronization delay, the output lead of the digital interactive feedback signal is dynamically adjusted to ensure real-time synchronization between the feedback signal and the movement rhythm.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. This invention introduces an adaptive compensation model for material properties based on physical principles to acquire environmental parameters, including site temperature and material aging index, in real time, and determines dynamic correction factors based on a preset mapping table; it uses the physical parameterization logic of the viscoelastic constitutive equation to perform weighted compensation and non-negative clamping correction on the dynamic characteristics of ground reaction force, effectively eliminating nonlinear interference from environmental factors such as site aging and temperature changes on mechanical data, restoring the true biomechanical state of athletes, and ensuring the objectivity and fairness of teaching evaluation under different seasons and different degrees of site aging.

[0040] 2. This invention utilizes a spatiotemporal alignment and coupling processing mechanism for visual and mechanical data. It employs an interpolation alignment algorithm to resolve the frequency difference between high-frequency mechanical sampling and low-frequency visual sampling, and establishes a frame-level mapping based on a unified timestamp benchmark. By combining a spatiotemporal aggregation function and an effective pressure threshold determination logic, it avoids division-by-zero singularity errors during the motion take-off period while completing the dimensionality reduction and fusion from the original pressure matrix to a multimodal motion interaction feature set. This solves the data heterogeneity problem between visual feature vectors and mechanical response matrices, ensuring the consistency and computability of multimodal data in the mathematical dimension.

[0041] 3. This invention uses a pre-trained mechanics-teaching semantic mapping network and feature space metric architecture to perform dimensionality reduction and whitening of real biomechanical feature vectors, and calculates the feature Euclidean distance between them and the standard motion mechanics template to quantify motion differences; combined with the maximum deviation dimension identification algorithm and the physical semantic restoration technology of factor loading matrix, it can accurately locate the physical dimension with the largest numerical deviation when the motion does not meet the standard and transform it into specific corrective guidance instructions, thereby transforming the invisible biomechanical force quality into visual motion guidance, solving the pain point of traditional teaching where only the form of the motion is known but not the essence of the force;

[0042] 4. This invention supports dual feedback of visual projection and tactile vibration through multimodal digital interactive feedback and predictive timing control strategies. It utilizes homography mapping and dynamic coordinate synchronization to ensure precise spatial alignment of light and shadow trajectories with the user's position, and modulates vibration frequencies based on the principle of bioresonance to distinguish the feedback texture of bones and muscles. Simultaneously, by monitoring the synchronization delay of ground-touching feedback and applying predictive event scheduling algorithms, it dynamically adjusts the output lead of feedback signals to compensate for hardware processing and transmission time, ensuring that the physical feedback signal accurately acts on the target action phase instant, thus realizing a zero-perception delay teaching closed loop where what you touch is what you get. Attached Figure Description

[0043] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0044] Figure 1This is a flowchart of the method of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0046] Example 1:

[0047] Please see Figure 1 A digital interactive teaching demonstration and feedback method for smart sports campuses, the method includes: acquiring real-time motion visual image data of the target moving object and field mechanical response data collected by the field sensor array;

[0048] Spatiotemporal alignment and coupling processing is performed on motion visual image data and field mechanical response data to determine a multimodal motion interaction feature set, which includes temporally synchronized posture kinematic features and ground reaction force dynamic features.

[0049] Based on a pre-built material property adaptive compensation model, the multimodal motion interaction feature set is denoised and corrected to determine the real biomechanical feature vector. The material property adaptive compensation model is configured to eliminate the nonlinear interference of the physical properties of the site materials on the mechanical data.

[0050] Real biomechanical feature vectors are input into a pre-trained mechanics-instructional semantic mapping network to determine target instructional feedback instructions, which include semantic suggestions for corrective action on motion quality. Based on the target instructional feedback instructions, digital interactive feedback signals are generated and used to drive the on-site feedback terminal to perform demonstration operations.

[0051] This embodiment details the execution architecture of the above-mentioned digital interactive teaching demonstration and feedback method for smart sports campuses; the system establishes a full-domain perception data stream, aiming to solve the defect that the existing single visual recognition cannot perceive the implicit force characteristics; this step acquires real-time motion visual image data of the target moving object through a high-speed visual capture unit, and this data is defined as an optical signal sequence containing the three-dimensional spatial position and posture changes of the moving object;

[0052] Meanwhile, the system collects site mechanical response data through a piezoelectric sensor array pre-embedded underground. This data is essentially a voltage or resistance change signal generated by the pressure on the site material, which is used to reflect the force distribution and strength of the ground.

[0053] The system addresses the differences in sampling rate and spatial coordinate system between visual and mechanical data by performing spatiotemporal alignment and coupling processing to generate a multimodal motion interaction feature set. This set maps key points of the human skeleton to the trajectory of the ground pressure center under a unified time reference and spatial coordinate system, encompassing both posture kinematics and ground reaction force dynamics. To eliminate the interference of site physical characteristics on teaching evaluation, the system introduces a material property adaptive compensation model. This model is specifically configured as a physical parameterized model based on the material viscoelastic constitutive equation, aiming to solve the training infeasibility problem caused by the inability of traditional neural network methods to obtain paired data of rigid ground truth values ​​and flexible track distortion values ​​at the same time and location.

[0054] The model's construction process is based on standard physical tests in a laboratory environment: material samples from the same batch as those used on the actual running track are selected, and dynamic thermomechanical analysis and impact rebound tests are conducted under controlled temperature gradients, such as -10℃ to 40℃, and aging simulation conditions. The stress-strain response curve and viscoelastic relaxation parameters of the material are then fitted. The system encapsulates these physical laws into deterministic computational logic, rather than a black-box neural network, to subtract the hysteresis and attenuation components caused by the material's viscoelasticity from the original mechanical response, thereby determining the true biomechanical characteristic vector. This vector eliminates the influence of environmental noise and nonlinear material deformation, simply reflecting the action of human muscles. Work and joint load; based on this, the system utilizes a pre-trained mechanics-instructional semantic mapping network to transform abstract mechanical features into understandable instructional language; this mapping network adopts a feature space metric architecture based on statistical manifold learning, specifically including: a feature whitening layer, used to eliminate the correlation of input feature dimensions and unify the variance scale; and a Mahalanobis distance metric layer, used to calculate the significance deviation of the current action vector in the statistical distribution of standard actions; during construction, the covariance matrix of the standard action dataset is solved using an eigenvalue decomposition algorithm to determine the principal component projection matrix and eigenvalue spectrum, thereby establishing a statistical mechanics template for standard actions;

[0055] Its input is a real biomechanical feature vector, and its output is the corresponding teaching action label. It determines the structured data package containing specific corrective actions, i.e., the target teaching feedback instruction. The system generates digital interactive feedback signals based on the feedback instructions, drives the on-site feedback terminal to perform demonstration operations, and realizes a teaching loop where you get what you touch.

[0056] This embodiment introduces an adaptive compensation model for material properties based on physical principles, which effectively eliminates the nonlinear interference of environmental factors such as aging and temperature on mechanical data, restoring the athlete's true biomechanical state. Combined with mechanics-teaching semantic mapping, the system transforms the invisible biomechanical force quality into visualized movement guidance for the first time in a smart sports scenario, solving the pain point in traditional teaching where students only know the form of the movement but not the essence of the force exertion.

[0057] Example 2:

[0058] Spatiotemporal alignment and coupling processing is performed on motion visual image data and field mechanical response data to determine a multimodal motion interaction feature set, including: extracting the temporal coordinates of key skeletal points from the motion visual image data; extracting the temporal data of pressure waveforms from the field mechanical response data; and performing frame-level mapping between the temporal coordinates of key skeletal points and the temporal data of pressure waveforms based on a unified timestamp reference to determine the multimodal motion interaction feature set.

[0059] This embodiment is a further specification of the spatiotemporal alignment and coupling processing steps in Embodiment 1; the system uses a pose estimation algorithm to process motion vision image data and extracts the temporal coordinates of key skeletal points; these coordinates include the three-dimensional coordinates of key parts such as the hip joint, knee joint, and ankle joint in each frame of the image; at the same time, the system extracts the temporal data of pressure waveforms from the raw signals of the field sensor array;

[0060] To establish the correlation between the two, this embodiment adopts a frame-level mapping strategy based on a unified timestamp reference. For the difference between the visual sampling frequency (typically 30-60Hz) and the mechanical sampling frequency (typically 100-1000Hz), the system uses an interpolation alignment algorithm to construct the mapping relationship. To ensure the executability of the non-causal interpolation algorithm in the real-time system, a fixed processing delay buffer is set up. Its length is set to be greater than or equal to This is the maximum value of the future window width required for subsequent differential calculations. At this point, the algorithm is processing the current time. Essentially lagging behind the system's physical real-time clock The calculation formula is:

[0061]

[0062] This ensures that the cache is already in the system memory. and The data collected at any given moment; specifically, for any mechanical sampling moment. The system locks the previous frame with an adjacent timestamp in the visual data stream. With the next frame Skeletal point coordinates and The synchronization time is calculated using a linear interpolation model.

[0063]

[0064] Among them, the coordinates of the skeleton points The world coordinate system is used, with the origin at... Set as the geometric center of the field sensing array plane, with the Z-axis pointing vertically upwards; in extraction and Previously, the system used Zhang Zhengyou's calibration method to calibrate the camera's intrinsic parameters and obtain the distortion coefficients. The calculation formula is as follows:

[0065]

[0066] in, Corresponding pixel coordinates of the original image , Let be the Euclidean distance from the pixel to the optical center of the image. By inversely solving or remapping the above model, the original pixel coordinates are geometrically corrected to eliminate barrel distortion caused by wide-angle lenses, thereby ensuring the continuity of visual features in the time domain. Based on this aligned vector, the calculation formula is as follows:

[0067]

[0068] in, for A set of multimodal motion interaction features at any given time;

[0069] Visually extracted and interpolated Time-space skeleton point coordinate vector;

[0070] The original pressure data matrix has the following dimensions: ; This represents the total number of sensing units. For time step;

[0071] This is a spatiotemporal aggregation function used to reduce the dimensionality of high-dimensional matrix data and adapt it to a visual frame. Specifically, this function performs two levels of operations: the first level is spatial dimensionality reduction, calculating the dimensionality at each time step. Total pressure scalar and pressure center coordinates The calculation formula is as follows:

[0072]

[0073] in For the first Each sensing unit in Pressure reading at any given time; The calculation employs the torque balance principle formula; to avoid the division-to-zero singularity error caused by the extremely small total pressure during the airborne phase, the algorithm presets an effective pressure threshold. For example, 10N, only when Perform the following division operation when the condition is met; otherwise, perform the following division operation: Set to the value or zero vector of the previous frame:

[0074]

[0075] in, For the first The fixed physical coordinates of each sensing unit in the field coordinate system; the field coordinate system has its origin at the center of the first sensing unit in the lower left corner of the sensing array matrix. The X-axis runs along the long side of the array, and the Y-axis runs along the short side; the second level is time-domain statistics, extracting time windows. Inside and The mean, peak value, and variance of the sequence are used to generate a one-dimensional mechanical eigenvector; specifically, this one-dimensional mechanical eigenvector... The dimension is 9, which is based on the , X-axis components, The mean, peak value, and variance of these three physical quantities for the Y-axis component are calculated respectively; at this time... The physical meaning is a vector concatenation operation, if the visual skeleton vector for The final multimodal feature set is dimensional. The total dimension is This step ensures The vector dimensions on both sides of the operator are compatible;

[0076] The effective pressure threshold is used to determine the validity of the sensor signal to avoid division by zero errors. Its value is determined based on the static noise floor of the site sensor array, which is usually set to 3-5 times the root mean square value of the background noise. In this embodiment, it is set to 10N to ensure that environmental micro-vibration interference is filtered out. The method for measuring the root mean square value of the background noise is as follows: under the unloaded state of the site, pressure data is continuously collected for 10 seconds, and its standard deviation is calculated as the noise floor.

[0077] This refers to the width of the time window; in this embodiment... Values That is, 200ms, which covers the average duration of the single-foot ground contact phase in a typical gait cycle; This is the vector concatenation operator;

[0078] This embodiment solves the problem of data heterogeneity between visual features and original mechanical responses by adopting a strategy of spatial aggregation followed by temporal statistics, ensuring the consistency and computability of the multimodal feature set in the mathematical dimension.

[0079] Example 3:

[0080] Based on a pre-built adaptive compensation model for material properties, the multimodal motion interaction feature set is denoised and corrected to determine the real biomechanical feature vector. This includes: obtaining the current site environmental parameters, which include site temperature data collected by temperature sensors and material aging index read from the maintenance database; and using the current site environmental parameters to perform query matching based on the preset environmental parameter-mechanical coefficient mapping table to determine the dynamic correction factors for material damping coefficient and rebound coefficient.

[0081] By using dynamic correction factors, weighted compensation calculations are performed on the ground reaction force dynamics characteristics in the multimodal motion interaction feature set to determine the true biomechanical feature vector.

[0082] This embodiment is a further refinement of the material property adaptive compensation step in Embodiment 1, focusing on solving the data distortion problem caused by changes in the site environment; the system reads the current site environment parameters in real time, which specifically include the site temperature data collected by the infrared temperature sensor and the material aging index read from the background maintenance database, the latter reflecting the degree of hardening caused by the service life of the runway;

[0083] It should be noted that the pre-built adaptive compensation model for material properties mentioned in this step is the same as the pre-built adaptive compensation model for material properties described in Example 1. Here, "pre-built" mainly refers to the model parameters, such as the mapping table coefficients, which have been experimentally calibrated and solidified during the construction phase. The system uses a pre-built environmental parameter-mechanical coefficient mapping table, which is essentially a set of discretized parameters of the viscoelastic constitutive equation of the material in Example 1, to query and determine the dynamic correction factor, i.e., the real-time physical coefficients in the constitutive equation. The calculation model for this factor is as follows:

[0084]

[0085] in, It is a dynamic correction factor that characterizes the degree of nonlinear attenuation or enhancement of the material's force transmission under the current environment;

[0086] This is a temperature effect function, reflecting the effect of temperature. The unit is ℃. Regarding the effect on the viscoelasticity of the material, this embodiment specifically uses a quadratic polynomial model. Perform fitting;

[0087] The aging effect function reflects the aging index. Regarding the effect on material hardness, this embodiment specifically adopts an exponential growth model. To characterize;

[0088] The basic calibration constant; in this embodiment Values To ensure the accuracy and transparency of the model parameters, this embodiment specifically explains how the above coefficients are obtained: The values ​​are derived from standard temperature change impact tests for specific track materials, such as polyurethane plastic track materials. The results are obtained by controlling the temperature gradient in the laboratory environment, for example, from -10℃ to 40℃, measuring the change in the material's resilience, and fitting the data using the least squares method.

[0089] This originates from accelerated aging tests of materials, which simulate different years using a UV aging chamber. The material hardening state is determined by measuring its Shore hardness change and establishing an exponential regression model; these pre-calibrated parameters are stored in the system's ROM for real-time retrieval.

[0090] Using the aforementioned factors, the system performs noise reduction correction on the original ground reaction force dynamic characteristics. This correction process is the inverse solution of the constitutive equation, calculating the true biomechanical feature vector. Considering that in actual signal processing, when the original force... Smaller and its rate of change When the noise is large, such as high-frequency flutter noise, directly subtracting the damping term may result in a non-physical negative value in the calculation result. This embodiment introduces non-negative clamping logic to ensure the physical validity of the result. The corrected calculation formula is as follows:

[0091]

[0092] in, The true biomechanical characteristic vector represents the force actually applied by the human body;

[0093] The raw mechanical characteristics collected by the sensor; The time derivative of the original mechanical characteristics is used to compensate for the hysteresis effect of the material; its calculation formula is:

[0094]

[0095] in The sampling interval is given in the formula. The acquisition of data at future times depends on the processing delay buffer defined in Example 2. Ensure that at the time of calculation At that time, the system had physically collected and stored data up to [date missing]. The original data; the coefficient set is .

[0096] Specifically, the five-point central difference algorithm is used to perform numerical differentiation on the discrete mechanical sampling sequence in order to reduce the interference of high-frequency noise on the derivative calculation;

[0097] The viscoelastic relaxation time constant of the material, expressed in seconds, is used to characterize the time order in which material deformation lags behind stress. To eliminate dimensional ambiguity, this embodiment uses the symbol... Characterizing the relaxation time constant, its physical essence is defined as the ratio of dynamic viscosity to elastic modulus. This ensures that the formula contains The unit is s N / s = N and Dimensionality is consistent; its value is based on current site temperature data. The temperature characteristic curve of the material, which is calculated using a pre-defined material viscoelasticity-temperature characteristic curve, is explicitly defined in this embodiment as an Arrhenius-type temperature-dependent function, and its calculation formula is as follows:

[0098]

[0099] in, The leading factor in the time dimension, in seconds. For activation energy, These are gas constants, obtained through dynamic thermomechanical analysis and fitting of material samples; for typical polyurethane running track materials, this embodiment sets a forward exponent factor. ,activation energy gas constant ;

[0100] The system directly inputs the real-time temperature. Calculate the accurate damping coefficient, rather than roughly looking it up in a table; The source is the algorithm logic, and the physical meaning is a one-sided constraint operator, used to eliminate negative value anomalies caused by derivative overshoot, ensuring that the output mechanical feature vector always maintains non-negative physical properties; this embodiment introduces environmental parameters to dynamically compensate mechanical data and combines them with boundary condition constraints, which is equivalent to configuring a perception filter for the intelligent system, enabling it to distinguish between collapses caused by physical deformation of the track and collapses caused by insufficient force exerted by students, thereby ensuring the objectivity and fairness of teaching evaluation under different seasons and different degrees of aging of the field.

[0101] Example 4:

[0102] The actual biomechanical feature vectors are input into a pre-defined mechanics-teaching semantic mapping network to determine the target teaching feedback instructions. This includes: performing feature dimensionality reduction on the actual biomechanical feature vectors to determine the core mechanics feature vectors; calculating the feature Euclidean distance between the core mechanics feature vectors and the pre-defined standard motion mechanics templates; determining the initial feedback semantics based on the feature Euclidean distance and the pre-defined semantic mapping rule table; and performing natural language encapsulation on the initial feedback semantics to determine the target teaching feedback instructions.

[0103] This embodiment is a concretization of the mechanics-teaching semantic mapping step in Embodiment 1. Given the extremely high dimensionality of real biomechanical feature vectors, the system performs feature dimensionality reduction and whitening processing to determine the core mechanics feature vectors. Before this step, to ensure the effectiveness and reproducibility of the feature transformation matrix, this embodiment details the methods for calculating the feature vector matrix. and eigenvalues The original training dataset consists of data collected by 500 volunteers with at least a Level 2 athlete qualification on a standard hard surface.

[0104] The data includes 2,000 valid samples for each type of teaching action, totaling more than 10,000 sets of data; the acquisition equipment uses a three-dimensional force table array with a sampling rate of 1000Hz and an optical motion capture system with a sampling rate of 200Hz.

[0105] The system calculates the covariance matrix of this large-scale standard dataset and performs eigenvalue decomposition to obtain the eigenvector matrix. and eigenvalues In specific operation, the system will use the original biomechanical feature vector Centralized processing ( (and multiply by the feature vector matrix calculated in advance based on the above dataset). Complete the orthogonal projection to obtain the principal component vectors. To eliminate differences in variance scales among principal components, the system performs Z-score standardization, calculated using the following formula:

[0106]

[0107] in, For the projected first principal component, For the corresponding eigenvalues, here The eigenvalues ​​refer to the eigenvalues ​​of the covariance matrix, distinct from those representing the dynamic correction factor in Example 3. Here, the denominator is taken as To achieve unit variance normalization, in order to conform to the statistical standardization definition;

[0108] To prevent the value from reaching a zero minimum, this embodiment sets an empirical value based on considerations of numerical calculation stability. This is to prevent computational overflow when the eigenvalue is zero;

[0109] This step eliminates the interference of large variance differences among different principal components on distance calculation, resulting in a more accurate and efficient vector generation. The feature space has an isotropic statistical distribution; the system calculates the difference between this vector and the preset standard action mechanics template; in this process, the mechanics-teaching semantic mapping network is constructed as a hybrid architecture with a fixed encoding layer and a learnable metric layer; the system measures the similarity between the current action and the standard prototype by calculating the Euclidean distance in the feature space, and the calculation formula is as follows:

[0110]

[0111] in, These are the values ​​of the standard template that have undergone the same whitening process; The source is a preset value, and its physical meaning is the dimension of the core mechanical feature vector, i.e., the number of principal components retained after dimensionality reduction; due to whitening processing, the Euclidean distance here... Mathematically equivalent to Mahalanobis distance, it accurately reflects the statistical significance bias of action patterns. To establish a mapping from abstract dimensionality-reduced features to specific natural language, the semantic mapping rule table is a factor loading matrix based on the PCA model. Constructed; matrix The calculation formula is:

[0112]

[0113] in, The eigenvector matrix, It is an eigenvalue diagonal matrix; elements in Characterized the first The original physical indicators, such as push-off force and knee angle, are related to the first The correlation coefficients of the principal components; the system's correlation coefficients. After performing the maximum variance rotation, each principal component dimension Bind to make its absolute load value Maximum physical index This gives the abstract dimension a clear physical semantics; based on the calculated distance, the system consults the rule table to determine the initial feedback semantics, and performs natural language encapsulation processing to generate a JSON-formatted target teaching feedback instruction containing numerical benchmarks.

[0114] Example 5:

[0115] Based on the feature Euclidean distance and a preset semantic mapping rule table, the initial feedback semantics are determined, including: the compliance threshold is set based on the statistical distribution of the standard action dataset, and in this embodiment, the specific value is 3 times the average distance within the standard action class, usually set between 3.0 and 5.0; it is determined whether the feature Euclidean distance is less than the preset compliance threshold; if the feature Euclidean distance is less than the compliance threshold, the initial feedback semantics are determined to be a positive reinforcement instruction; if the feature Euclidean distance is greater than or equal to the compliance threshold, the dimension with the largest absolute value of numerical deviation from the standard action mechanical template in the core mechanical feature vector is identified, and the initial feedback semantics are determined to be a corrective guidance instruction based on the dimension.

[0116] This embodiment is a logical refinement of the initial feedback semantic determination step in Embodiment 4. The system sets a compliance threshold to define whether an action meets the standard. In response to a feature Euclidean distance less than the preset compliance threshold, the system determines that the action deviation is within the allowable range and identifies the initial feedback semantic as a positive reinforcement instruction. Conversely, in response to a feature Euclidean distance greater than or equal to the compliance threshold, the system determines that the action has a significant defect and executes the maximum deviation dimension identification algorithm, which is based on the core mechanical feature vector determined in Embodiment 4. Compared with the whitened standard template The calculation is performed using the following formula:

[0117]

[0118] in Index the feature dimension with the largest deviation; in order to abstract the dimension index Transforming the instructions into specific corrective guidance, the system performs a physical semantics restoration operation: calling the factor loading matrix defined in Example 4. In the Find the row index with the largest absolute value of the load coefficient in the column. The index The specific physical meaning corresponding to the original input vector, for example: Corresponding knee flexion angle; system-integrated deviation sign With load symbol Determine the actual deviation direction of the physical quantity. For example, a combination of positive and negative signs indicates that the angle is too small, and retrieve the corresponding corrective semantics from the pre-set corpus, such as increasing the degree of knee flexion.

[0119] This embodiment achieves a teaching strategy of grasping the main contradiction by accurately locating the dimension with the largest deviation and restoring its physical essence.

[0120] Example 6:

[0121] The method also includes: obtaining the probability confidence of the output of the mechanics-teaching semantic mapping network as the confidence of the mechanics-teaching semantic mapping; determining whether the confidence of the mechanics-teaching semantic mapping meets the preset confidence interval requirements; if not, triggering the auxiliary calibration process, generating a calibration prompt signal to obtain manual correction input, and adjusting the weight parameters of the mechanics-teaching semantic mapping network according to the manual correction input.

[0122] This embodiment is a further improvement on the method of Embodiment 1, adding confidence assessment and manual calibration mechanisms; the system obtains the probability distribution of the neural network output; given that the implementation method of Embodiment 4 uses Euclidean distance... Instead of using a statistical feature Euclidean distance metric architecture, which requires complex pairwise training deep metric learning networks, this step defines a nonlinear mapping function from the feature space distance to the probability space to compute the confidence of the mechanics-educational semantic mapping. The specific calculation formula adopts the Gaussian radial basis function form, and its calculation formula is as follows:

[0123]

[0124] in, The characteristic Euclidean distance between the core mechanical feature vector calculated in Example 4 and the standard template; This is a preset sensitivity hyperparameter, the value of which is determined by the within-class variance statistics of the standard action dataset. The specific calculation formula is as follows:

[0125]

[0126] in, The number of samples in the standard dataset. For sample features, The template mean is set to 1.5 in this embodiment to control the rate at which confidence decreases with increasing distance.

[0127] The system determines whether the confidence level falls within a preset confidence interval, for example... ; In response to the confidence level not meeting the credible interval requirement, i.e. The system identifies the current action pattern as rare or difficult to judge and triggers an auxiliary calibration process. In this process, the system sends a calibration prompt signal to the teacher and displays the raw data. The system also obtains the correct action evaluation manually entered by the teacher as the input for manual correction.

[0128] The system uses this input as labeled data and fine-tunes the network's weight parameters through backpropagation. Specifically, for the prototype network architecture used in this system, adjusting the weight parameters refers to updating the feature coordinates of the standard motion mechanics template. When manual correction input confirms that the current motion, although far away, is still a valid motion, the system applies a moving average update strategy.

[0129]

[0130] in, Given the current input vector, For the update rate, e.g., 0.01;

[0131] This mechanism allows the standard template to gradually accommodate more reasonable individual differences in actions as the teaching process accumulates, solving the uncertainty problem of AI models when facing atypical actions, and giving the system the ability to continuously learn and evolve, so that its judgment logic becomes closer to the experience of human experts as the usage time increases.

[0132] Example 7:

[0133] Based on the target teaching feedback instructions, a digital interactive feedback signal is generated and driven to perform demonstration operations on the on-site feedback terminal. This includes: parsing the target teaching feedback instructions and determining the feedback modality type, which includes visual projection mode and tactile vibration mode; if the feedback modality type is visual projection mode, a dynamic light and shadow signal containing the motion correction trajectory is generated based on the motion correction data in the target teaching feedback instructions and projected onto the field surface; if the feedback modality type is tactile vibration mode, a pulse signal of a specific frequency, such as 60Hz or 250Hz, is generated according to a preset bioresonance frequency mapping table and drives the actuator built into the field to generate physical feedback.

[0134] This embodiment is a concretization of the step of generating digital interactive feedback signals in embodiment 1; the system parses the instruction to select a suitable feedback channel; in response to determining that the feedback modality type is visual projection modality, the system reads the motion correction data in the instruction and extracts the projection coordinates of the standard motion three-dimensional trajectory on the horizontal ground;

[0135] To address the spatial alignment issue between the standard trajectory and the current position of the moving object, the system performs dynamic coordinate synchronization before generating the light and shadow signal: acquiring the physical coordinates of the user's current footing point captured by visual sensing. The translation calibration vector is calculated using the following formula:

[0136]

[0137] Apply this offset to the entire sequence to obtain the calibrated physical coordinates. The system performs planar homography mapping using a pre-calibrated homography matrix. homography matrix The calibration method is as follows: place 5 known physical coordinates at the four corners and the center of the site. Infrared reflective markers are used to project a checkerboard pattern onto the image, and a vision system captures the pixel coordinates of the markers within the image. Solving the system of equations using the direct linear transformation algorithm get matrix The formula for converting ground physical coordinates to projector pixel coordinates is:

[0138]

[0139] in, The scaling factor for homogeneous coordinate transformation; is the pixel coordinate vector of the projector's image plane; This is the calibrated site physical coordinate vector;

[0140] In response to determining the feedback mode type as tactile vibration mode, the system generates physical feedback based on the degree of deviation in the command; specifically, a linear mapping algorithm is used to determine the vibration intensity, with the following logic: when the characteristic Euclidean distance... When, set When the feature Euclidean distance When the time is right, use the following formula to calculate:

[0141]

[0142] in, The compliance threshold for haptic feedback triggering; This represents the upper limit of the deviation corresponding to the maximum vibration intensity; This is an interval constraint function used to constrain the calculation results within the effective duty cycle range of the pulse width modulation signal, ensuring that the output value is within... Between, i.e., minimum duty cycle To maximum duty cycle ;

[0143] Meanwhile, in order to meet the generation requirements of specific frequencies, the system parses the body part labels in the target teaching feedback instructions and uses a preset bioresonance frequency mapping table to determine the pulse frequency. For example, for areas with high bone rigidity, such as the tibia in the lower leg, a high-frequency signal can be set. To enhance the sense of penetration; for areas with thicker soft tissue, such as the thighs, set a low-frequency signal. To enhance the sense of diffusion; ultimately, the driving signal is generated as This achieves dual modulation feedback of frequency (i.e., texture) and intensity (i.e. magnitude).

[0144] Monitor the ground contact feedback synchronization delay from the moment the target moving object touches the field and generates field mechanical response data to the execution of demonstration operations by the on-site feedback terminal; based on the ground contact feedback synchronization delay, dynamically adjust the output lead of the digital interactive feedback signal to ensure real-time synchronization between the feedback signal and the movement rhythm.

[0145] This embodiment involves fine-grained control of the timing of feedback signals to solve the system delay problem; the system monitors the grounding feedback synchronization delay in real time. The specific implementation method of this monitoring is as follows: during the initialization phase, the system sends a data packet with a sending timestamp. The test pulse command; for the visual projection mode, high-speed photodiodes installed at the edge of the field are used to capture the physical moment when the projected light spot reaches the ground. For tactile vibration modes, accelerometers buried underground are used to capture the moment of vibration onset. System calculation The grounding feedback synchronization delay, including the total time spent on hardware processing and physical transmission, is obtained. And store it in the system configuration parameters;

[0146] This refers to the time difference between the signal generated when the foot touches the ground and the feedback execution; the system is based on real-time monitored step frequency. Phase angle with target action The calculation is performed. First, the normalized phase percentage within the action cycle is defined. Taking the standing long jump as an example, the moment of takeoff is defined as... Maximum takeoff height is The moment of landing is If the teaching objective is to correct the posture at the "moment of maximum force exertion," analysis of the standard mechanics template shows that the maximum push-off force occurs before takeoff. In this case, set It needs to be clarified that here... The unit is radians, expressed as a normalized phase percentage. Multiply The conversion formula is as follows: To adapt to the subsequent trigonometric function and angular frequency calculation formulas;

[0147] For example, the phase corresponding to the point of maximum force, calculate the precise triggering time of the digital interactive feedback signal;

[0148] To implement this dynamic adjustment, the system adopts a predictive event scheduling strategy: based on real-time step frequency. Predicting the timing of the next ground contact event. To ensure that the physical point of application of the feedback signal, such as the arrival of light or shadow or the occurrence of vibration, accurately falls on the user's action. At the phase moment, the system calculates the absolute trigger time. The calculation formula has been revised as follows:

[0149]

[0150] Among them, item This represents the expected target physical moment, i.e., the phase at which the action arrives in the next cycle. The absolute point in time; subtract This is to compensate for the time consumed by hardware processing and transmission, also known as output advance adjustment; when the system's internal high-precision clock arrives... Upon receiving the command, control instructions are immediately sent; this mechanism deducts hardware processing time from the timeline in advance. This ensures that the feedback physical signal can accurately respond when the user's action reaches the preset phase angle. It acts on the senses instantly, eliminating the delay in perception.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital interactive teaching demonstration and feedback method for smart sports campuses, characterized in that: The method includes: Acquire real-time motion visual image data of the target moving object and field mechanical response data collected by the field sensor array; The motion visual image data and the field mechanical response data are spatiotemporally aligned and coupled to determine a multimodal motion interaction feature set, wherein the multimodal motion interaction feature set includes temporally synchronized posture kinematic features and ground reaction force dynamic features. Based on a pre-built material property adaptive compensation model, the multimodal motion interaction feature set is denoised and corrected to determine the real biomechanical feature vector. The material property adaptive compensation model is configured to eliminate the nonlinear interference of the physical properties of the site materials on the mechanical data. The real biomechanical feature vectors are input into a pre-trained mechanics-instructional semantic mapping network to determine the target instruction for instruction, which includes semantic suggestions for corrective action on the quality of movement. Based on the target teaching feedback instructions, a digital interactive feedback signal is generated and drives the on-site feedback terminal to perform demonstration operations.

2. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The process of performing spatiotemporal alignment and coupling processing on the motion visual image data and the site mechanical response data to determine a multimodal motion interaction feature set includes: Extract the temporal coordinates of key skeleton points from the motion visual image data; Extract the time-series data of the pressure waveform from the site mechanical response data; Based on a unified timestamp benchmark, the temporal coordinates of the key skeletal points are mapped to the temporal data of the pressure waveform at the frame level to determine the multimodal motion interaction feature set.

3. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The pre-built material property adaptive compensation model performs denoising and correction processing on the multimodal motion interaction feature set to determine the true biomechanical feature vector, including: Obtain current site environmental parameters, including site temperature data collected by temperature sensors and material aging index read from a maintenance database; Based on the preset environmental parameter-mechanical coefficient mapping table, the dynamic correction factors for the material damping coefficient and rebound coefficient are determined by querying and matching the current site environmental parameters. Using the dynamic correction factor, a weighted compensation calculation is performed on the ground reaction force dynamics features in the multimodal motion interaction feature set to determine the true biomechanical feature vector.

4. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The step of inputting the real biomechanical feature vector into a preset mechanics-teaching semantic mapping network to determine the target teaching feedback instruction includes: The real biomechanical feature vectors are subjected to feature dimensionality reduction processing to determine the core mechanical feature vectors; Calculate the feature Euclidean distance between the core mechanical feature vector and the preset standard motion mechanical template; The initial feedback semantics are determined based on the Euclidean distance of the features and the preset semantic mapping rule table; The initial feedback semantics are encapsulated in natural language to determine the target teaching feedback instruction.

5. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 4, characterized in that, The step of determining the initial feedback semantics based on the feature Euclidean distance and a preset semantic mapping rule table includes: Determine whether the Euclidean distance of the feature is less than a preset compliance threshold; If the feature Euclidean distance is less than the compliance threshold, the initial feedback semantics are determined to be an affirmative reinforcement instruction; If the Euclidean distance of the feature is greater than or equal to the compliance threshold, identify the dimension in the core mechanical feature vector that has the largest absolute value of numerical deviation from the standard motion mechanical template, and determine the initial feedback semantics as a corrective guidance instruction based on the dimension.

6. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The method further includes: The probability confidence score output by the mechanics-teaching semantic mapping network is obtained as the mechanics-teaching semantic mapping confidence score. Determine whether the confidence level of the mechanics-teaching semantic mapping meets the preset confidence interval requirement; If the conditions are not met, an auxiliary calibration process is triggered to generate a calibration prompt signal to obtain manual correction input, and the weight parameters of the mechanics-teaching semantic mapping network are adjusted according to the manual correction input.

7. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The step of generating digital interactive feedback signals and driving the on-site feedback terminal to perform demonstration operations based on the target teaching feedback instructions includes: The target teaching feedback instructions are analyzed to determine the feedback modality type, which includes visual projection modality and tactile vibration modality; If the feedback modality type is visual projection modality, a dynamic light and shadow signal containing the action correction trajectory is generated based on the action correction data in the target teaching feedback instruction and projected onto the field surface. If the feedback mode type is a tactile vibration mode, a pulse signal of a specific frequency is generated, which drives the actuator built into the site to produce physical feedback.

8. The method for digital interactive teaching demonstration and feedback in smart sports campuses according to claim 1, characterized in that, The method further includes: Monitor the ground-touch feedback synchronization delay from the moment the target moving object touches the field and generates the field mechanical response data to the moment the on-site feedback terminal executes the demonstration operation. Based on the ground contact feedback synchronization delay, the output lead of the digital interactive feedback signal is dynamically adjusted to ensure real-time synchronization between the feedback signal and the movement rhythm.

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