Interactive physical education method and system based on augmented reality driving

Abstract

The invention relates to the technical field of physical training intellectualization and man-machine interaction, in particular to an interactive physical education method and system based on augmented reality driving, and the method comprises the following steps: S1, collecting three-dimensional motion track data and muscle activation time sequence data; s2, generating kinematic deviation parameters and physiological deviation parameters; s3, generating an AR correction instruction set containing a joint pose correction vector and a muscle activation intensity adjustment suggestion based on the kinematics deviation parameter and the physiology deviation parameter; s4, projecting the AR correction instruction set to the field of view of the trainee in real time through AR display equipment; and S5, generating a physical education efficiency report containing the action precision improvement index and the neuromuscular control optimization scheme. According to the intelligent physical education system, by fusing multi-source motion perception, deviation analysis and AR visual correction, synchronous optimization of motion precision and neuromuscular control ability is realized, and an intelligent physical education system with real-time interaction characteristics is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sports training and human-computer interaction, and particularly to an interactive sports teaching method and system driven by augmented reality. Background Art

[0002] With the continuous improvement of sports levels and health needs, many training methods have begun to focus on movement quality and sports safety; however, teaching methods based on traditional coaches' oral guidance or offline video demonstrations have limitations in actual applications, such as being difficult to quantify the accuracy of movements and unable to deeply monitor the muscle activation state, making it difficult to fully meet the needs of personalized and scientific training; especially for training projects that require precise mastery of movement details and improvement of neuromuscular control ability, there is a lack of real-time, visual, and interactive technical means, resulting in learners being unable to correct incorrect movements or optimize muscle activation patterns in a timely manner.

[0003] Although existing technologies have introduced inertial sensors or bio-signal acquisition devices, they generally have problems such as low data integration, non-intuitive feedback methods, and difficulty in dynamically projecting correction instructions, making it difficult to simultaneously consider multi-dimensional deviation analysis of kinematics and physiology; with the gradual maturity of augmented reality technology, how to organically combine joint trajectories, electromyography signals, and AR visual correction instructions to achieve precise quantification and real-time guidance of movement deviations has become a technical problem in the current field of interactive sports teaching. Summary of the Invention

[0004] Based on the above purposes, the present invention provides an interactive sports teaching method and system driven by augmented reality.

[0005] An interactive sports teaching method driven by augmented reality includes the following steps: S1: Synchronously collect three-dimensional motion trajectory data and muscle activation timing data through an inertial sensor array and an electromyography sensor worn on the trainee. S2: Compare the three-dimensional motion trajectory data with a standard action database to generate kinematic deviation parameters, and at the same time analyze the difference between the muscle activation timing data and the standard muscle activation pattern to generate physiological deviation parameters. S3: Based on the kinematic deviation parameters and physiological deviation parameters, generate an AR correction instruction set including joint pose correction vectors and muscle activation intensity adjustment suggestions. S4: Project the AR correction instruction set onto the trainee's field of view in real time through an AR display device, where the joint pose correction vectors are presented by guiding dynamic arrows, and the muscle activation intensity adjustment suggestions are presented by color-coded pressure maps. S5: Generate a sports teaching effectiveness report containing an action accuracy improvement index and a neuromuscular control optimization plan based on the improvement rate of kinematic deviation parameters and the matching degree of physiological deviation parameters after the trainee executes the AR correction instruction set.

[0006] Optionally, the S1 specifically includes: S11: Deploy an inertial sensor array at predetermined nodes of the trainee. The inertial sensor array includes at least 9 MEMS inertial measurement units, which are respectively fixed to the head, shoulder joint, elbow joint, wrist joint, lumbar spine, hip joint, knee joint and ankle joint; at the same time, attach electromyography sensors to the surface of the target muscle groups, and the target muscle groups at least include the anterior deltoid muscle, the rectus femoris muscle and the external oblique abdominal muscle; S12: Establish a time synchronization protocol between the inertial sensor array and the electromyography sensors, and send a unified timestamp signal to all sensors through a wireless communication module to ensure the timing alignment of the data collected by each sensor; S13: Real-time capture the joint angular velocity data and acceleration data of the trainee during the movement process through the inertial sensor array, and calculate the three-dimensional space coordinate data of each joint based on the strap-down inertial navigation algorithm; S14: Synchronously collect the surface electromyography signals of the target muscle groups through the electromyography sensors, and perform band-pass filtering and rectification processing on the original electromyography signals to extract the muscle activation timing data.

[0007] Optionally, the S2 specifically includes: S21: Compare the three-dimensional motion trajectory data obtained based on S1 with the preset joint angles, body postures and action rhythms in the standard action database, obtain a trajectory similarity index using a matching degree algorithm, and calculate the difference amounts of the trainee's actual trajectory and the database standard trajectory in terms of pose, speed and timing through statistical methods, and generate kinematic deviation parameters; S22: Compare the muscle activation timing data obtained based on S1 with the maximum activation amplitude, peak occurrence time and duration in the standard muscle activation pattern, and use point-by-point difference analysis and amplitude normalization processing to evaluate the deviation degree of the trainee's actual muscle activation process from the standard pattern, and generate physiological deviation parameters.

[0008] Optionally, the S21 specifically includes: S211: Perform time series sampling on the three-dimensional motion trajectory data collected by the trainee during the training process to generate a continuous set of trajectory points; S212: Pair the set of trajectory points with the trajectory points of the corresponding standard action in the standard action database point by point, calculate the spatial difference value between each pair of trajectory points, and average all the difference values to form a trajectory similarity index; S213: Calculate the average difference value between the trainee and the standard action in terms of joint pose according to the pose angle difference of each sampling point; S214: Based on the speed change of the sampling points, count the difference value between the trainee and the standard action in terms of action execution speed, and at the same time evaluate the time alignment degree of the same action stage to form a speed difference value and a timing difference value; S215: Set weight coefficients according to the importance of the pose difference value, speed difference value and timing difference value, and perform weighted synthesis on the three to generate a kinematic deviation parameter.

[0009] Optionally, the S22 specifically includes: S221: Pair the muscle activation timing data of the trainee with the corresponding timing sampling points in the standard muscle activation pattern, and calculate the point-by-point amplitude deviation ; S222: Perform amplitude normalization on the difference value to obtain a normalized difference value ; S223: Combine the difference degree between the trainee and the standard muscle activation pattern in terms of the peak appearance time and duration, record the average normalized difference value of the trainee in this peak interval , and combine the maximum difference value, peak time offset and activation duration offset to form a physiological deviation parameter , and its expression is: , where represents the maximum value of the normalized difference value; represents the difference amount between the peak appearance time of the trainee and the standard peak appearance time; represents the difference amount between the peak duration of the trainee and the standard peak duration; represents the weight coefficient used to quantify the influence degree of each difference.

[0010] Optionally, the S3 specifically includes: S31: According to the kinematic deviation parameter obtained in S2, extract the pose deviation value of each joint, and define the deviation vector of each joint as , and its direction is consistent with the spatial angle between the actual movement direction and the standard movement direction; and generate a corresponding correction vector according to this deviation vector, and the calculation formula is: , where represents the th joint correction vector; represents the th joint spatial pose deviation vector; is the th joint correction coefficient; S32: According to the physiological deviation parameters obtained in S2, extract the deviation value of the activation intensity of the target muscle group. Let the actual muscle activation value of the trainee be , the standard activation value be , then the difference between the two is , and generate a muscle activation intensity adjustment suggestion based on this difference , and its calculation formula is: , where represents the muscle activation intensity adjustment suggestion of the target muscle group; represents the muscle activation deviation value of the target muscle group; represents the adjustment ratio coefficient for this muscle group; S33: Combine the correction vectors of each joint with the muscle activation intensity adjustment suggestion into an AR correction instruction set.

[0011] Optionally, the specific steps of S4 include: S41: Import the AR correction instruction set generated in S3 into the rendering module of the AR display device, and after the trainee enters the specified practice area, start the real-time visual tracking function of the device to detect the current position and action state of the trainee; S42: Perform visualization processing on the correction vectors of each joint, map the correction vector of each joint in the instruction set to a dynamic arrow symbol, align the starting point of the arrow with the joint position of the trainee, the arrow direction is the same as the direction of the correction vector, and the arrow length is proportional to the amplitude of the correction vector; S43: Perform color-coded mapping on the muscle activation intensity adjustment suggestion, overlay a pressure layer on the surface position of the corresponding muscle group of the trainee, and display different color gradients according to the positive and negative and magnitude of the adjustment suggestion. The saturation of the color is proportional to the adjustment amplitude, which is used to prompt the contraction or relaxation demand of the target muscle group; S44: Overlay and present the dynamic arrow and the color-coded pressure map in the trainee's field of view, and achieve multi-angle dynamic alignment in combination with the attitude tracking function of the AR display device, so that the arrow and the pressure map are updated in real time when the trainee's action changes.

[0012] Optionally, the specific steps of S5 include: S51: After the trainee completes the training actions under the guidance of the AR correction instruction set, re-collect the three-dimensional motion trajectory data and muscle activation time series data of the trainee, and calculate the kinematic deviation parameters and physiological deviation parameters under the same action unit before and after training respectively; S52: Perform difference analysis on the kinematic deviation parameters after training and the corresponding parameters before training, and calculate the improvement rate of the kinematic deviation parameters , defined as the percentage of the difference to the initial deviation value; S53: Calculate the matching degree between the trained muscle activation time series data and the standard muscle activation pattern, measure the similarity of the electromyogram signal waveform using the normalized correlation index, and obtain the physiological parameter matching degree index. ; S54: Generate an action precision improvement index based on the improvement rate of kinematic deviation parameters and the matching degree of physiological parameters, reflecting the overall training effectiveness. S55: Output a neuromuscular control optimization plan based on the action precision improvement index, including the muscle groups that need to be strengthened in training, recommended action rhythms, and posture maintenance suggestions, and finally form a physical education teaching effectiveness report and output it in a structured manner.

[0013] Optionally, the specific content of S54 includes: S541: Weight and synthesize the improvement rate of kinematic deviation parameters and the physiological parameter matching degree index according to the following formula to obtain the action precision improvement index. The formula is: , where: represents the action precision improvement index; represents the improvement rate of kinematic deviation parameters; represents the physiological parameter matching degree; and are the weight coefficients corresponding to the kinematic and physiological indexes respectively.

[0014] An augmented reality-driven interactive physical education teaching system for implementing the above-mentioned augmented reality-driven interactive physical education teaching method, including the following modules: Multi-source sign collection module: Used to deploy an inertial sensor array at the key joint positions of the trainee and attach electromyogram sensors at the positions of the target muscle groups to collect the three-dimensional motion trajectory data and muscle activation time series data of the trainee respectively. Action deviation analysis module: Used to receive the data collected by the multi-source sign collection module, compare the three-dimensional motion trajectory data with the standard action database to calculate and generate kinematic deviation parameters, and at the same time perform matching analysis on the muscle activation time series data and the standard electromyogram pattern, and output physiological deviation parameters. Correction instruction generation module: Used to generate joint pose correction vectors for each key joint and muscle activation intensity adjustment suggestions for the corresponding target muscle groups respectively according to the kinematic deviation parameters and physiological deviation parameters, and finally form an AR correction instruction set. AR instruction visualization module: Used to project the AR correction instruction set constructed by the correction instruction generation module onto the AR display device, and present the joint posture adjustment direction in the form of dynamic arrows and present the muscle activation adjustment prompt in the form of a color-coded map. Training effect evaluation module: It is used to re-collect the motion and electromyography data of the trainee after completing the AR correction training, calculate the kinematic deviation improvement rate and physiological parameter matching degree based on the change of deviation parameters before and after training, and generate an action accuracy improvement index therefrom. Optimization strategy output module: It is used to comprehensively output a neuromuscular control optimization plan including posture correction direction, muscle group strengthening training plan and action rhythm suggestions according to the action accuracy improvement index and the deviation source.

[0015] Advantages of the present invention: In the present invention, by synergistically collecting the three-dimensional motion trajectory data and muscle activation timing data of the trainee during the training process through inertial sensors and electromyography sensors, high-precision and multi-dimensional perception of the key action execution process is realized.

[0016] In the present invention, through kinematic and physiological deviation analysis, the posture error and abnormal muscle group activation during action execution are accurately identified, and an AR correction instruction set including joint pose correction vectors and muscle activation intensity adjustment suggestions is further generated, which improves the visualization degree and pertinence of action feedback, significantly improves the refinement degree and intervention effect of individual training in physical education teaching, and solves the problems of lagging feedback and lack of interactivity of traditional guidance means. Description of the drawings

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic diagram of an interactive physical education teaching method according to an embodiment of the present invention; Figure 2 It is a schematic diagram of an interactive physical education teaching system according to an embodiment of the present invention. Detailed implementation manners

[0019] The present invention will be described in detail below with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings part is only for more specifically describing the embodiments, and is not intended to specifically limit the present invention.

[0020] It should be noted that in the specification, references to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. Additionally, when describing a specific feature, structure, or characteristic in connection with an embodiment, implementing such feature, structure, or characteristic in connection with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0021] Generally, terms can be understood, at least in part, from their use in context. For example, at least in part depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood to not necessarily be intended to convey a set of exclusive factors, but rather, at least in part depending on the context, to allow for the existence of other factors that may not be explicitly described.

[0022] As Figure 1 shown, an interactive sports teaching method driven by augmented reality includes the following steps: S1: Synchronously collect three-dimensional motion trajectory data and muscle activation timing data through an inertial sensor array and electromyography sensors worn on the trainee. S2: Compare the three-dimensional motion trajectory data with a standard action database to generate a kinematic deviation parameter, and at the same time analyze the difference between the muscle activation timing data and the standard muscle activation pattern to generate a physiological deviation parameter. S3: Based on the kinematic deviation parameter and the physiological deviation parameter, generate an AR correction instruction set including a joint pose correction vector and a muscle activation intensity adjustment suggestion. S4: Project the AR correction instruction set onto the trainee's field of view in real time through an AR display device, where the joint pose correction vector is presented with dynamic arrows and the muscle activation intensity adjustment suggestion is presented with a color-coded pressure map. S5: Generate a sports teaching effectiveness report including an action accuracy improvement index and a neuromuscular control optimization plan according to the improvement rate of the kinematic deviation parameter and the matching degree of the physiological deviation parameter after the trainee executes the AR correction instruction set.

[0023] S1 specifically includes: S11: Deploy an inertial sensor array at predetermined nodes of the trainee. The inertial sensor array includes at least 9 MEMS (Micro-Electro-Mechanical System) inertial measurement units (IMUs), which are respectively fixed to the head, shoulder joint, elbow joint, wrist joint, lumbar spine, hip joint, knee joint, and ankle joint; at the same time, attach electromyography sensors to the surface of the target muscle groups, and the target muscle groups at least include the anterior deltoid, rectus femoris, and external oblique abdominal muscles. S12: Establish a time synchronization protocol between the inertial sensor array and the electromyography sensor, and send a unified timestamp signal to all sensors through the wireless communication module to ensure the alignment of the data acquisition timings of each sensor; S13: Real-time capture the joint angular velocity data and acceleration data during the trainee's movement through the inertial sensor array, and calculate the three-dimensional space coordinate data of each joint based on the strapdown inertial navigation algorithm; the three-dimensional coordinate data is calculated by the following formula: , where represents the moment The three-dimensional space coordinates of each joint; represents the moment The acceleration vector measured by the inertial sensor; represents the initial velocity vector; represents the initial spatial position; is the time variable used in the integration, representing any moment within the integration interval; represents the time variable The change amount of, which is used for the cumulative addition of the continuous change of time in the integration operation; S14: Synchronously collect the surface electromyography signals (sEMG) of the target muscle groups through the electromyography sensor, and perform band-pass filtering (10Hz - 500Hz) and rectification processing on the original electromyography signals to extract the muscle activation timing data; The muscle activation intensity signal is extracted according to the following formula: , where represents the moment The muscle activation intensity at; represents the moment The band-pass filtered electromyography signal at; represents the time width of the sliding window, which is used for the extraction of smooth electromyography data; the above steps, by arranging inertial measurement units at key motion nodes and combining with the electromyography acquisition of the target muscle groups, not only ensure the high-precision spatial capture of the motion behavior, but also realize the real-time synchronous monitoring of the muscle activation situation, providing basic data support for subsequent motion deviation analysis.

[0024] S2 specifically includes: S21: Based on the three-dimensional motion trajectory data obtained in S1, compare it with the preset joint angles, body postures, and action rhythm information in the standard action database, use the matching degree algorithm to obtain the trajectory similarity index, and calculate the difference amounts of the trainee's actual trajectory and the database standard trajectory in terms of pose, speed, and timing through statistical methods to generate kinematic deviation parameters; S22: Based on the muscle activation timing data obtained in S1, compare it with the maximum activation amplitude, the peak occurrence time, and the duration in the standard muscle activation pattern. Use point-by-point difference analysis and amplitude normalization to evaluate the deviation degree of the actual muscle activation process of the trainee from the standard pattern, and generate a physiological deviation parameter; the above steps can accurately evaluate the trainee's movements at the kinematic and physiological levels by quantifying the differences between the three-dimensional motion trajectory data and the standard action database in S2, and by making point-by-point comparisons between the muscle activation timing and the standard muscle activation pattern, providing a reliable basis for the generation of subsequent AR correction instructions.

[0025] S21 specifically includes: S211: Perform time series sampling on the three-dimensional motion trajectory data collected during the training of the trainee to generate a continuous set of trajectory points; S212: Pair the set of trajectory points with the trajectory points of the corresponding standard action in the standard action database point by point, calculate the spatial difference value between each pair of trajectory points, and perform an average process on all the difference values to form a trajectory similarity index for measuring the deviation degree of the overall motion trajectory; S213: Calculate the average difference value of the trainee and the standard action in terms of joint pose according to the pose angle difference of each sampling point; S214: Based on the speed change situation of the sampling points, statistically calculate the difference value of the trainee and the standard action in terms of the action execution speed, and at the same time evaluate the time alignment degree of the same action phase to form a speed difference value and a timing difference value; S215: Set weight coefficients according to the importance degrees of the pose difference value, the speed difference value, and the timing difference value, and perform weighted synthesis on the three to generate a kinematic deviation parameter.

[0026] The specific calculation process is as follows: First, first denote the three-dimensional motion trajectory sampling points of the trainee as , and denote the corresponding trajectory sampling points in the standard action database as , and use to represent that the sampling order is from 1 to ; Then, calculate the single-point Euclidean distance through the following formula : ; Next, take the arithmetic mean of the distances of all sampling points to obtain the average distance , and use this average distance to reflect the overall offset of the trajectory. The trajectory similarity index is defined as follows: ; , where: represents the The Euclidean distance of a sampling point; Indicates the total number of trajectory samplings; Indicates the average distance of all sampling points; Indicates the trajectory similarity index; Indicates the subscript of the sampling point; Finally, by statistical methods, the difference amounts in terms of pose, velocity, and timing are calculated respectively. The joint pose difference, velocity vector difference, and action rhythm difference of each sampling point are respectively defined as , and its average value at sampling points is denoted as , and the kinematic deviation parameters are summarized using weighted coefficients. The specific formula is as follows: ; ; ; ; where: Indicates the joint pose difference of the th sampling point; Indicates the velocity vector difference of the th sampling point; Indicates the action rhythm difference of the th sampling point; Respectively indicate the average difference amounts of pose, velocity, and timing; Indicates the weighted coefficients assigned to each difference amount; Indicates the finally generated kinematic deviation parameter.

[0027] S22 specifically includes: S221: Pair the muscle activation timing data of the trainee with the corresponding timing sampling points in the standard muscle activation pattern, and calculate the point-by-point amplitude deviation , and the specific calculation formula is: , where Indicates the muscle activation amplitude detected at the th sampling point; Indicates the standard muscle activation amplitude at the th sampling point; Indicates the point-by-point difference between the two; S222: Perform amplitude normalization on the difference to obtain the normalized difference , and the formula is: , where: Indicates the maximum activation amplitude of the standard muscle activation pattern within the entire timing range; Indicates the The normalized difference corresponding to a sampling point S223: Combine the difference degree between the trainee and the standard muscle activation pattern in terms of the peak appearance time and duration, and record the average normalized difference of the trainee within this peak interval and form a physiological deviation parameter by synthesizing the maximum difference, the peak time offset, and the activation duration offset Its expression is where represents the maximum value of the normalized difference represents the difference between the peak appearance time of the trainee and the standard peak appearance time represents the difference between the peak duration of the trainee and the standard peak duration represents the weight coefficient used to quantify the influence degree of each difference; through the above steps, by adopting point-by-point difference analysis and combining amplitude normalization, the amplitude deviation of the trainee relative to the standard muscle activation pattern can be accurately reflected; at the same time, based on the difference evaluation of the peak appearance time and duration, a quantitative index is provided for the matching degree of the overall muscle activation process, so that the physiological deviation evaluation is more accurate and targeted, providing a scientific basis for subsequent movement correction and neuromuscular control optimization

[0028] S3 specifically includes S31: According to the kinematic deviation parameters obtained in S2, extract the posture deviation values of each joint, and define the deviation vector of each joint as whose direction is consistent with the spatial angle between the actual movement direction and the standard movement direction, and the magnitude represents the deviation amplitude; and generate a corresponding correction vector according to this deviation vector, and the calculation formula is where represents the correction vector of the th joint represents the spatial posture deviation vector of the th joint is the correction coefficient of the th joint, preset according to the difficulty of the training action; the negative sign indicates that the correction direction is opposite to the deviation direction S32: According to the physiological deviation parameters obtained in S2, extract the deviation value of the activation intensity of the target muscle group. Let the actual muscle activation value of the trainee be and the standard activation value be then the difference between the two is and generate a muscle activation intensity adjustment suggestion according to this difference whose calculation formula is where represents the muscle activation intensity adjustment suggestion of the target muscle group Indicates the muscle activation deviation value of the target muscle group; Indicates the adjustment ratio coefficient for this muscle group, used to control the feedback intensity; a negative sign indicates that when the deviation value is positive, it is recommended to reduce the activation intensity, and vice versa, it is recommended to increase it; S33: Combine the correction vectors of each joint with the muscle activation intensity adjustment suggestions into an AR correction instruction set; the above steps can map the kinematic deviation parameters and physiological deviation parameters to the joint pose correction vectors and muscle strength adjustment suggestions respectively, so as to present the dual feedback information of spatial posture and muscle activation in the same AR correction instruction set, helping the trainee to synchronously optimize the movement posture and neuromuscular control.

[0029] S4 specifically includes: S41: Import the AR correction instruction set generated by S3 into the rendering module of the AR display device, and after the trainee enters the specified practice area, start the real-time visual tracking function of the device to detect the current position and movement state of the trainee; S42: Visualize the correction vectors of each joint, map the correction vector of each joint in the instruction set to a dynamic arrow symbol, align the starting point of the arrow with the trainee's joint position, the arrow direction is the same as the correction vector direction, and the arrow length is proportional to the magnitude of the correction vector; S43: Perform color-coded mapping on the muscle activation intensity adjustment suggestions, overlay a pressure layer on the surface position of the trainee's corresponding muscle group, and display different color gradients according to the positive and negative and magnitude of the adjustment suggestions. The saturation of the color is proportional to the adjustment amplitude, used to prompt the contraction or relaxation requirements of the target muscle group; S44: Overlay and present the dynamic arrow and the color-coded pressure map in the trainee's field of view, and achieve multi-angle dynamic alignment by combining the pose tracking function of the AR display device, so that the arrow and the pressure map are updated in real time when the trainee's movement changes, so as to achieve synchronous visual correction guidance for joint posture and muscle activation intensity; the above steps convert the joint correction vector and the muscle activation adjustment suggestion into a dynamic arrow and a color pressure map respectively through the AR display device, which can not only accurately prompt the joint direction and intensity requirements visually, but also perform real-time tracking and update of different movement stages during training, significantly improving the immediacy and accuracy of movement correction.

[0030] S5 specifically includes: S51: After the trainee completes the training actions under the guidance of the AR correction instruction set, re-collect its three-dimensional motion trajectory data and muscle activation time series data, and calculate the kinematic deviation parameters and physiological deviation parameters under the same motion unit before and after training respectively; S52: Perform difference analysis on the kinematic deviation parameters after training and the corresponding parameters before training, and calculate the improvement rate of the kinematic deviation parameters , defined as the percentage of the difference to the initial deviation value, is used to reflect the improvement degree of the action execution accuracy; the specific calculation formula is: , where represents the improvement rate of the kinematic deviation parameter; represents the kinematic deviation parameter before training; represents the kinematic deviation parameter after training; S53: Calculate the matching degree between the muscle activation time series data after training and the standard muscle activation pattern, and use the normalized correlation index to measure the similarity of the electromyogram signal waveform to obtain the physiological parameter matching degree index , which is used to evaluate the consistency of neuromuscular control; the calculation formula is as follows: , where represents the physiological parameter matching degree index; represents the muscle activation value at the th sampling point after training; represents the muscle activation value at the th sampling point in the standard pattern; represents the total number of samplings; S54: Generate an action accuracy improvement index based on the improvement rate of the kinematic deviation parameter and the physiological parameter matching degree, reflecting the overall training effect; S55: Output a neuromuscular control optimization plan according to the action accuracy improvement index, including the muscle groups that need to be intensively trained, the recommended action rhythm and posture maintenance suggestions, and finally form a physical education teaching effectiveness report and output it in a structured manner; the above steps compare the changes in the motion deviation and electromyogram pattern before and after training, systematically quantify the improvement degree of the trainee in action accuracy and neuromuscular control, and generate a teaching effectiveness report containing quantitative indicators and training suggestions, providing a scientific basis and data support for personalized training.

[0031] S54 specifically includes: S541: Weight and synthesize the improvement rate of the kinematic deviation parameter and the physiological parameter matching degree index according to the following formula to obtain the action accuracy improvement index, and the formula is: , where: represents the action accuracy improvement index; represents the improvement rate of the kinematic deviation parameter; represents the physiological parameter matching degree; and are the weight coefficients corresponding to the kinematic and physiological indexes respectively; S542: According to the calculated action accuracy improvement index , to judge the level of the overall training effectiveness, the closer the index value is to the expected maximum value, the closer the movement posture and muscle activation process are to the standard mode, thus providing a quantitative basis for subsequent neuromuscular control optimization and personalized teaching plans.

[0032] In S55, according to the movement precision improvement index , the steps to generate a neuromuscular control optimization plan are as follows: S551: According to the calculation result of the movement precision improvement index , compare it with the preset threshold . If , it is determined that the training meets the standard, and consolidated training suggestions are output; if , the neuromuscular control optimization plan generation process is started; S552: When , analyze the relative proportion of the improvement rate of kinematic deviation parameters and the matching degree of physiological parameters . If , it is determined that the kinematic improvement is insufficient; if , it is determined that the muscle control is insufficient; where and are the set improvement rate and matching degree evaluation thresholds respectively; S553: When , according to the main distribution area of the posture deviation, combined with the historical record of the correction vector, output specific joint posture correction suggestions, clearly indicating the joint numbers to be corrected, the range of angle deviation, and the recommended adjustment direction; S554: When , extract the muscle groups with the normalized matching difference greater than the set threshold , and combined with the peak amplitude error and timing offset, output a list of target muscle groups that need to be intensively trained, and put forward improvement suggestions including activation rhythm, muscle group coordination order, and relaxation control; S555: Combine the posture correction suggestions and muscle activation optimization suggestions to generate a neuromuscular control optimization plan, and output it in a structured form in the report for subsequent personalized teaching path configuration.

[0033] As Figure 2 shown, an augmented reality-driven interactive sports teaching system for implementing the above-mentioned augmented reality-driven interactive sports teaching method includes the following modules: Multi-source physical sign acquisition module: used to deploy an inertial sensor array at the key joint positions of the trainee and attach electromyography sensors at the positions of the target muscle groups to collect the three-dimensional motion trajectory data and muscle activation timing data of the trainee respectively; Motion Deviation Analysis Module: It is used to receive the data collected by the multi-source vital sign acquisition module, compare the three-dimensional motion trajectory data with the standard motion database to calculate and generate kinematic deviation parameters, and at the same time match and analyze the muscle activation timing data with the standard electromyogram pattern to output physiological deviation parameters; Correction Instruction Generation Module: It is used to generate joint pose correction vectors for each key joint and muscle activation intensity adjustment suggestions for the corresponding target muscle groups respectively according to the kinematic deviation parameters and physiological deviation parameters, and finally form an AR correction instruction set; AR Instruction Visualization Module: It is used to project the AR correction instruction set constructed by the correction instruction generation module onto the AR display device, present the joint posture adjustment direction in the form of dynamic arrows, and present the muscle activation adjustment prompt in the form of a color-coded map; Training Effect Evaluation Module: It is used to re-collect the motion and electromyogram data of the trainee after completing the AR correction training, calculate the kinematic deviation improvement rate and physiological parameter matching degree based on the change of deviation parameters before and after training, and generate an action accuracy improvement index therefrom; Optimization Strategy Output Module: It is used to comprehensively output a neuromuscular control optimization plan including posture correction direction, muscle group strengthening training plan and action rhythm suggestions according to the action accuracy improvement index and the deviation source.

[0034] This invention covers any alternatives, modifications, equivalent methods and solutions made within the essence and scope of this invention. For the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention without the description of these details. In addition, well-known methods, processes, procedures, components and circuits are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0035] The above are only the preferred embodiments of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this invention.

Claims

1. An interactive sports teaching method driven by augmented reality, characterized in that: The following steps are involved: S1: Synchronously collect three-dimensional motion trajectory data and muscle activation timing data through inertial sensor arrays and electromyographic sensors worn on trainees; S2: Compare the three-dimensional motion trajectory data with the standard action database to generate kinematic deviation parameters, and analyze the difference between the muscle activation timing data and the standard muscle activation pattern to generate physiological deviation parameters; S3: Based on the kinematic deviation parameters and physiological deviation parameters, an AR correction instruction set including joint posture correction vectors and muscle activation intensity adjustment suggestions is generated; S4: The AR correction instruction set is projected into the trainee's field of view in real time through the AR display device, where the joint posture correction vector is presented as a dynamic arrow guide, and the muscle activation intensity adjustment suggestion is presented as a color-coded pressure map; S5: Generate a physical education teaching effectiveness report including the movement accuracy improvement index and neuromuscular control optimization plan based on the improvement rate of kinematic deviation parameters and the matching degree of physiological deviation parameters after the trainees execute the AR correction instruction set.

2. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S1 specifically includes: S11: deploying an inertial sensor array at a predetermined node of the trainee, wherein the inertial sensor array includes at least 9 MEMS inertial measurement units, which are respectively fixed to the head, shoulder joint, elbow joint, wrist joint, lumbar spine, hip joint, knee joint and ankle joint; and attaching myoelectric sensors to the surface of the target muscle group, wherein the target muscle group includes at least the anterior deltoid muscle, the rectus femoris muscle and the external oblique muscle of the abdomen; S12: Establish a time synchronization protocol between the inertial sensor array and the electromyographic sensor, and send a unified timestamp signal to all sensors through the wireless communication module to ensure that the timing of data collected by each sensor is aligned; S13: The inertial sensor array is used to capture the joint angular velocity data and acceleration data of the trainee in real time during the movement, and the three-dimensional spatial coordinate data of each joint is obtained based on the strapdown inertial navigation algorithm; S14: synchronously collect surface electromyographic signals of the target muscle group through electromyographic sensors, perform bandpass filtering and rectification on the original electromyographic signals, and extract muscle activation timing data.

3. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S2 specifically includes: S21: Based on the three-dimensional motion trajectory data obtained in S1, the data is compared with the joint angles, body postures and movement rhythm information preset in the standard action database, and the trajectory similarity index is obtained using the matching algorithm. The difference between the actual trajectory of the trainee and the standard trajectory in the database in terms of posture, speed and timing is calculated by statistical methods to generate kinematic deviation parameters; S22: Based on the muscle activation timing data obtained in S1, it is compared with the maximum activation amplitude, peak occurrence time and duration in the standard muscle activation pattern. Point-by-point difference analysis and amplitude normalization processing are used to evaluate the degree of deviation between the trainees' actual muscle activation process and the standard pattern, and generate physiological deviation parameters.

4. The interactive sports teaching method based on augmented reality driving according to claim 3 is characterized in that: The S21 specifically includes: S211: performing time series sampling on the three-dimensional motion trajectory data collected by the trainee during the training process to generate a continuous trajectory point set; S212: Pairing the trajectory point set with the trajectory points of the corresponding standard action in the standard action database point by point, calculating the spatial difference value between each pair of trajectory points, and averaging all the difference values ​​to form a trajectory similarity index; S213: Calculate the average difference value of the joint posture between the trainee and the standard action according to the posture angle difference of each sampling point; S214: Based on the speed change of the sampling points, the difference value of the trainee's and the standard action's execution speed is counted, and the time alignment degree of the same action phase is evaluated at the same time, so as to form a speed difference value and a timing difference value; S215: Setting weight coefficients according to the importance of the posture difference value, the speed difference value, and the timing difference value, performing weighted synthesis on the three, and generating kinematic deviation parameters.

5. The interactive sports teaching method based on augmented reality driving according to claim 3 is characterized in that: The S22 specifically includes: S221: Pair the trainee's muscle activation timing data with the corresponding timing sampling points in the standard muscle activation pattern point by point, and calculate the point-by-point amplitude deviation ; S222: Perform amplitude normalization processing on the difference to obtain a normalized difference ; S223: Based on the difference between the trainee's peak occurrence time and duration and the standard muscle activation pattern, record the trainee's average normalized difference within the peak interval , and the maximum difference, peak time offset and activation duration offset are combined to form the physiological deviation parameter , whose expression is: ,in, Indicates the maximum value of the normalized difference; It indicates the difference between the trainee's peak moment and the standard peak moment; It indicates the difference between the trainee's peak duration and the standard peak duration; Represents the weight coefficient used to quantify the impact of each difference.

6. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S3 specifically includes: S31: According to the kinematic deviation parameters obtained in S2, the posture deviation value of each joint is extracted, and the deviation vector of each joint is defined as , whose direction is consistent with the spatial angle between the actual motion direction and the standard motion direction; and the corresponding correction vector is generated according to the deviation vector, and the calculation formula is: ,in, Indicates Correction vectors for each joint; Indicates The spatial posture deviation vector of each joint; For the Correction factor for each joint; S32: According to the physiological deviation parameters obtained in S2, the activation intensity deviation value of the target muscle group is extracted, and the actual muscle activation value of the trainee is set to , the standard activation value is , then the difference between the two is , and generate muscle activation intensity adjustment suggestions based on the difference , and its calculation formula is: ,in, Indicates the activation intensity adjustment suggestions for the target muscle groups; Indicates the muscle activation deviation value of the target muscle group; It represents the adjustment ratio coefficient for this muscle group; S33: Combine the correction vectors of each joint and the muscle activation intensity adjustment suggestions into an AR correction instruction set.

7. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S4 specifically includes: S41: importing the AR correction instruction set generated in S3 into the rendering module of the AR display device, and after the trainee enters the designated practice area, starting the real-time visual tracking function of the device to detect the trainee's current position and action status; S42: Visualize the correction vector of each joint, map the correction vector of each joint in the instruction set into a dynamic arrow symbol, align the starting point of the arrow with the trainee's joint position, the direction of the arrow is the same as the direction of the correction vector, and the length of the arrow is proportional to the amplitude of the correction vector; S43: Color-code and map the muscle activation intensity adjustment suggestions, superimpose a pressure layer on the surface of the corresponding muscle group of the trainee, and display different color gradients according to the positive and negative and size of the adjustment suggestions. The color saturation is proportional to the adjustment amplitude, which is used to prompt the contraction or relaxation needs of the target muscle group; S44: The dynamic arrow and the color-coded pressure map are superimposed and presented in the trainee's field of view, and the posture tracking function of the AR display device is combined to achieve multi-angle dynamic alignment, so that the arrow and the pressure map are updated in real time when the trainee's movements change.

8. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S5 specifically includes: S51: After the trainee completes the training action under the guidance of the AR correction instruction set, the three-dimensional motion trajectory data and muscle activation timing data are re-collected, and the kinematic deviation parameters and physiological deviation parameters under the same action unit before and after training are calculated respectively; S52: Perform difference analysis on the kinematic deviation parameters after training and the corresponding parameters before training, and calculate the improvement rate of the kinematic deviation parameters , defined as the percentage of the difference to the initial deviation value; S53: Calculate the matching degree between the post-training muscle activation timing data and the standard muscle activation pattern, use the normalized correlation index to measure the similarity of the electromyographic signal waveform, and obtain the physiological parameter matching index ; S54: Generate the movement accuracy improvement index based on the kinematic deviation parameter improvement rate and the matching degree of physiological parameters to reflect the overall training effect; S55: Based on the movement accuracy improvement index, output the neuromuscular control optimization plan, including the muscle groups that need to be strengthened, the recommended movement rhythm and posture maintenance suggestions, and finally form a physical education teaching effectiveness report and output it in a structured manner.

9. The interactive sports teaching method based on augmented reality driving according to claim 1 is characterized in that: The S54 specifically includes: S541: Improve the kinematic deviation parameter Matching index with physiological parameters These two indicators are weighted and synthesized according to the following formula to obtain the action accuracy improvement index, which is: ,in: Indicates the action accuracy improvement index; represents the improvement rate of kinematic deviation parameters; Indicates the matching degree of physiological parameters; and are the weight coefficients corresponding to kinematic and physiological indicators, respectively.

10. An interactive physical education teaching system based on augmented reality driving, used to implement the interactive physical education teaching method based on augmented reality driving as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Multi-source vital signs acquisition module: used to deploy inertial sensor arrays at key joints of trainees and attach electromyographic sensors to target muscle groups to respectively collect trainees' three-dimensional motion trajectory data and muscle activation timing data; Action deviation analysis module: used to receive data collected by the multi-source vital signs acquisition module, compare the three-dimensional motion trajectory data with the standard motion database, calculate and generate kinematic deviation parameters, and match and analyze the muscle activation timing data with the standard electromyographic pattern to output physiological deviation parameters; Correction instruction generation module: used to generate joint posture correction vectors for each key joint and muscle activation intensity adjustment suggestions for the corresponding target muscle groups based on kinematic deviation parameters and physiological deviation parameters, and finally form an AR correction instruction set; AR instruction visualization module: used to project the AR correction instruction set constructed by the correction instruction generation module into the AR display device, and present the joint posture adjustment direction in the form of dynamic arrow guidance, and present the muscle activation adjustment prompt in the form of color coding diagram; Training effect evaluation module: used to re-collect the trainee's motion and electromyographic data after completing AR correction training, calculate the kinematic deviation improvement rate and the matching degree of physiological parameters based on the deviation parameter changes before and after training, and generate the movement accuracy improvement index; Optimization strategy output module: used to comprehensively output a neuromuscular control optimization plan including posture correction direction, muscle group strengthening training plan and movement rhythm recommendation based on the movement accuracy improvement index and deviation source.

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