CN111275339A - Indoor snow sliding teaching action analysis and correction method and system and readable storage medium - Google Patents
Indoor snow sliding teaching action analysis and correction method and system and readable storage medium Download PDFInfo
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- G09B19/003—Repetitive work cycles; Sequence of movements
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Abstract
The invention discloses an indoor snow sliding teaching action analysis and correction method, a system and a readable storage medium, wherein the method comprises the following steps: acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data; retrieving a preset reference action model according to the identity information and the training level of the student; and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student. According to the invention, the posture evaluation index is calculated by acquiring the posture data of the student in real time, and the skiing teaching action is corrected by comparing the posture evaluation index with the reference action model, so that the learning efficiency of the student is improved, the skiing action of the student is corrected, and the safety risk caused by the action error of the student is reduced.
Description
Technical Field
The invention relates to the field of analysis and correction of skiing teaching actions, in particular to an analysis and correction method and system for indoor skiing teaching actions and a readable storage medium.
Background
With the improvement of the living standard of people, the success of winter and Australia is achieved, and the guidance of national policies, more and more people participate in skiing sports, but because the traditional skiing field is limited by environmental factors such as regions, seasons, climate and the like, for most skiers, a great amount of time, energy and financial resources are required to be invested in learning skiing, and the learning efficiency is low when the skiers learn skiing on a natural snow slope. In view of this, indoor simulation skiing machines are popular with more and more skiers, but the indoor simulation skiing machines in the market are far from the real skiing scene, and similar to the traditional skiing field, face the predicament of teacher and higher training cost, and the function of the existing indoor skiing training system lacks effective analysis and correction on the action of the student, thereby resulting in low learning efficiency of the student, irregular action and safety risk, and lower safety.
Therefore, it is necessary to develop an indoor skiing teaching action analysis and correction method.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides an indoor skiing teaching action analysis and correction method, an indoor skiing teaching action analysis and correction system and a readable storage medium.
In order to solve the technical problem, the first aspect of the invention discloses an indoor skiing teaching action analysis and correction method, which comprises the following steps:
acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data;
retrieving a preset reference action model according to the identity information and the training level of the student;
and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
In this scheme, the posture data includes: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
In this embodiment, the posture evaluation index includes: the included angle between the head and the horizontal direction, the bent angle between the two arms, the included angle between the trunk and the thigh and the bent angle between the knee joint.
In this scheme, the trainee identity information includes: age, height, sex; the training grades are divided according to training duration acquired by the trainees, and specifically include: primary, intermediate, and advanced.
In the scheme, the action correction comprises real-time action correction prompt information and a training situation learning suggestion, the real-time action correction information is transmitted in a voice mode, and the training situation learning suggestion comprises single action adjustment, single action training time length and coach one-to-one correction guidance.
In this scheme, the preset reference motion model includes a single board training reference motion model and a double board training reference motion model.
In the scheme, the posture evaluation index of the student is compared with the reference action model, and meanwhile, the swinging times of two arms and the deflection times of the trunk of the student within the set time are also obtained,
and respectively comparing the swinging times of the arms and the deflection times of the trunk of the student within a set time with preset threshold values, and if the swinging times of the arms and the deflection times of the trunk of the student are greater than the preset threshold values, performing action correction prompting.
The invention provides an indoor skiing teaching action analysis and correction system, which comprises a memory and a processor, wherein the memory comprises an indoor skiing teaching action analysis and correction method program, and the indoor skiing teaching action analysis and correction method program realizes the following steps when being executed by the processor:
acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data;
acquiring a retrieval preset reference action model according to the identity information and the training level of the student;
and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
In this scheme, the posture data includes: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes an indoor skiing teaching action analysis and correction method program, and when the indoor skiing teaching action analysis and correction method program is executed by a processor, the method implements any one of the steps of the indoor skiing teaching action analysis and correction method described above.
The invention discloses an indoor skiing teaching action analysis and correction method, a system and a readable storage medium.
Drawings
Fig. 1 shows a flow chart of an indoor skiing teaching action analysis and correction method of the invention.
Fig. 2 shows a flow chart of action correction based on the number of swinging of the arms and the number of deflection of the trunk within a set time according to the present invention.
Fig. 3 shows a block diagram of an indoor snow sliding teaching action analysis and correction system.
Detailed description of the invention
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The indoor skiing teaching action analysis and correction method is mainly suitable for indoor training systems of gliding sports, such as a simulation skiing machine, obtains posture evaluation indexes by obtaining posture data of students, and prompts action correction by comparing action deviation results with a reference action model. Of course, the present invention is not limited to the type of indoor skiing machine, and any technical solution adopting the present invention will fall into the protection scope of the present invention.
Fig. 1 shows a flow chart of an indoor skiing teaching action analysis and correction method of the invention.
As shown in fig. 1, a first aspect of the present invention discloses an indoor skiing teaching action analysis and correction method, including:
s102, acquiring posture data of the trainees, and constructing a posture evaluation index by using the posture data;
s104, retrieving a preset reference action model according to the identity information and the training level of the student;
s106, comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is larger than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
In a specific embodiment, the posture data of the trainee during training can be acquired through the node sensor, for example, the node sensor can be embedded in the training clothes, or can be a sensor with a wireless transmission function which is independently pasted outside the training clothes.
In this scheme, the posture data includes: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
In a specific embodiment, the head node data may be a vector of a straight line where a vertical axis of the head is located, the two-arm node data may be a straight line vector where the upper arm and the lower arm are located, the trunk node data may be a straight line vector where the upper body trunk is located, the hip joint node data may be coordinate data of the hip joint, the thigh node data may be a straight line vector where the thigh is located, and the shank node data may be a straight line vector where the shank is located.
In this embodiment, the posture evaluation index includes: the included angle between the head and the horizontal direction, the bent angle between the two arms, the included angle between the trunk and the thigh and the bent angle between the knee joint.
In a specific embodiment, the included angle between the head and the horizontal direction can be calculated through the included angle between the vector of the straight line where the vertical axis of the head is located and the horizontal direction, the bending angle of the two arms can be calculated through the linear vector of the large arm and the linear vector of the small arm, the included angle between the trunk and the thigh can be calculated through the linear vector of the trunk of the upper body and the linear vector of the thigh, and the bending included angle of the knee joint can be calculated through the linear vector of the thigh and the linear vector of the shank.
And acquiring the posture evaluation index as an index to be compared of the trainee.
In this scheme, the trainee identity information includes: age, height, sex; the training grades are divided according to training duration acquired by the trainees, and specifically include: primary, intermediate, and advanced.
It should be noted that the reference motion models of students of different ages, heights and sexes are different, and different reference motion models are established according to different identity information categories, and can be divided into male students and female students according to the gender, then can be divided into children, teenagers (12 to 18 years old) and adults (more than 18 years old) according to the ages, wherein the children can be divided into preschool children (3 to 6 years old) and school-age children (6 to 12 years old), and the reference motion models are further subdivided according to the heights, such as less than 1.2 meters, 1.2 meters to 1.6 meters, and 1.6 meters to 2 meters.
More specifically, after the trainees are classified and modeled through the identity information, the constructed reference motion model can be optimized through training levels, and is divided into a primary level, a middle level and a high level according to the training duration acquired by the trainees, wherein the primary level is set to be less than 10 training hours, the middle level is set to be more than 10 hours and less than 20 hours, and the high level is set to be more than 20 hours.
In the scheme, the action correction comprises real-time action correction prompt information and a training situation learning suggestion, the real-time action correction information is transmitted in a voice mode, and the training situation learning suggestion comprises single action adjustment, single action training time length and coach one-to-one correction guidance.
In a specific embodiment, the real-time motion correction information is communicated by voice, for example, when the angle between the head of the student and the horizontal direction is larger than a preset angle value, the student is in a head-down or head-up state, and the student is prompted to keep a correct head posture by voice.
For example, if the included angle between the trunk and the thigh is larger than a preset value, if the preset value is 70 degrees, and the included angle between the trunk and the thigh of the student is 180 degrees in real time, it is indicated that the student is in an upright state at the moment, and the student is prompted to adjust the included angle between the trunk and the thigh through voice to keep a correct body posture.
After the trainees finish training once, the trainees can consult the training suggestions of learning conditions after training to adjust the next training, if a certain single action is corrected for a plurality of times, the trainees are advised to strengthen the single action and give suggestions of the training time length of the single action, or the trainees are advised to correct the instruction one by one.
In this scheme, the preset reference motion model includes a single board training reference motion model and a double board training reference motion model.
It should be noted that different equipment motions are different, for example, the motions can be divided into a single-board training reference motion model and a double-board training reference motion model, and the reference motion model is finely divided according to different equipment, so that the training is more efficient and accurate.
Fig. 2 shows a flow chart of action correction based on the number of swinging of the arms and the number of deflection of the trunk within a set time according to the present invention.
In this scheme, when acquireing student's physique data, still acquire student's both arms swing number of times, trunk skew number of times in the settlement time, specific step is:
202, acquiring the swinging times of two arms and the deflection times of a trunk of a student within a set time;
204 the times of double-arm swinging and trunk deflection of the trainee in a set time,
respectively comparing with preset threshold values, and if the threshold values are larger than the preset threshold values, performing action correction prompting.
It should be noted that, in a specific embodiment, a single training process of the trainee may be divided into a starting stage, a control stage, and an ending stage, different thresholds may be set at different stages for the number of swinging the arms and the number of deflection of the trunk within a set time, for example, the threshold at the starting stage may be smaller than the threshold at the control stage, and the threshold at the ending stage is also smaller than the threshold at the control stage, so as to better conform to the training process, the number of swinging the arms and the number of deflection of the trunk within the set time of the trainee may be obtained by a video acquisition system, for example, the number of swinging the arms of the primary trainee within 10 seconds is greater than 7, which indicates that the trainee has too frequent actions for operating the trainee, and the probability of being in a high-speed sliding state is high, and based on that the current training level of the trainee is not suitable, thereby reducing the coast speed. For example, if the number of times of trunk deflection of the primary student is more than 5 times in 10 seconds, the sliding state becomes frequent, and the safety risk exists for the primary student, and at this time, corrective action prompt can be performed.
Fig. 3 shows a block diagram of an indoor snow sliding teaching action analysis and correction system.
The second aspect of the present invention provides an indoor skiing teaching action analysis and correction system, including a memory 31 and a processor 32, where the memory includes an indoor skiing teaching action analysis and correction method program, and when the processor executes the indoor skiing teaching action analysis and correction method program, the following steps are implemented:
acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data;
acquiring a preset reference action model according to the identity information and the training level of the student;
and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
In this scheme, the posture data includes: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
In a specific embodiment, the head node data may be a vector of a straight line where a vertical axis of the head is located, the two-arm node data may be a straight line vector where the upper arm and the lower arm are located, the trunk node data may be a straight line vector where the upper body trunk is located, the hip joint node data may be coordinate data of the hip joint, the thigh node data may be a straight line vector where the thigh is located, and the shank node data may be a straight line vector where the shank is located.
In this embodiment, the posture evaluation index includes: the included angle between the head and the horizontal direction, the bent angle between the two arms, the included angle between the trunk and the thigh and the bent angle between the knee joint.
In a specific embodiment, the included angle between the head and the horizontal direction can be calculated through the included angle between the vector of the straight line where the vertical axis of the head is located and the horizontal direction, the bending angle of the two arms can be calculated through the linear vector of the large arm and the linear vector of the small arm, the included angle between the trunk and the thigh can be calculated through the linear vector of the trunk of the upper body and the linear vector of the thigh, and the bending included angle of the knee joint can be calculated through the linear vector of the thigh and the linear vector of the shank.
And acquiring the posture evaluation index as an index to be compared of the trainee.
In this scheme, the trainee identity information includes: age, height, sex; the training grades are divided according to training duration acquired by the trainees, and specifically include: primary, intermediate, and advanced.
It should be noted that the reference motion models of students of different ages, heights and sexes are different, and different reference motion models are established according to different identity information categories, and can be divided into male students and female students according to the gender, then can be divided into children, teenagers (12 to 18 years old) and adults (more than 18 years old) according to the ages, wherein the children can be divided into preschool children (3 to 6 years old) and school-age children (6 to 12 years old), and the reference motion models are further subdivided according to the heights, such as less than 1.2 meters, 1.2 meters to 1.6 meters, and 1.6 meters to 2 meters.
More specifically, after the identity information classification modeling, the constructed reference motion model can be optimized through the training level, and is divided into a primary level, a middle level and a high level according to the training duration acquired by the trainee, wherein the primary level is set below 10 training hours, the middle level is set above 10 hours and below 20 hours, and the high level is set above 20 hours.
In the scheme, the action correction comprises real-time action correction prompt information and a training situation learning suggestion, the real-time action correction information is transmitted in a voice mode, and the training situation learning suggestion comprises single action adjustment, single action training time length and coach one-to-one correction guidance.
In a specific embodiment, the real-time motion correction information is communicated by voice, for example, when the angle between the head of the student and the horizontal direction is larger than a preset angle value, the student is in a head-down or head-up state, and the student is prompted to keep a correct head posture by voice.
For example, if the included angle between the trunk and the thigh is larger than a preset value, if the preset value is 70 degrees, and the included angle between the trunk and the thigh of the student is 180 degrees in real time, it is indicated that the student is in an upright state at the moment, and the student is prompted to adjust the included angle between the trunk and the thigh through voice to keep a correct body posture.
After the trainees finish training once, the trainees can consult the training suggestions of learning conditions after training to adjust the next training, if a certain single action is corrected for a plurality of times, the trainees are advised to strengthen the single action and give suggestions of the training time length of the single action, or the trainees are advised to correct the instruction one by one.
In this scheme, the preset reference motion model includes a single board training reference motion model and a double board training reference motion model.
It should be noted that different equipment motions are different, for example, the motions can be divided into a single-board training reference motion model and a double-board training reference motion model, and the reference motion model is finely divided according to different equipment, so that the training is more efficient and accurate.
In the scheme, the physical evaluation index of the trainee is compared with the reference action model, and simultaneously, the trainee also compares the physical evaluation index with the reference action model
The physical data of the student are obtained, the times of swinging of two arms and the times of deflection of the trunk of the student within a set time are also obtained,
and respectively comparing the swinging times of the arms and the deflection times of the trunk of the student within a set time with preset threshold values, and if the swinging times of the arms and the deflection times of the trunk of the student are greater than the preset threshold values, performing action correction prompting.
It should be noted that, in a specific embodiment, a single training process of the trainee may be divided into a starting stage, a control stage, and an ending stage, different thresholds may be set at different stages for the number of swinging the arms and the number of deflection of the trunk within a set time, for example, the threshold at the starting stage may be smaller than the threshold at the control stage, and the threshold at the ending stage is also smaller than the threshold at the control stage, so as to better conform to the training process, the number of swinging the arms and the number of deflection of the trunk within the set time of the trainee may be obtained by a video acquisition system, for example, the number of swinging the arms of the primary trainee within 10 seconds is greater than 7, which indicates that the trainee has too frequent actions for operating the trainee, and the probability of being in a high-speed sliding state is high, and based on that the current training level of the trainee is not suitable, thereby reducing the coast speed. For example, if the number of times of trunk deflection of the primary student is more than 5 times in 10 seconds, the sliding state becomes frequent, and the safety risk exists for the primary student, and at this time, corrective action prompt can be performed.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes an indoor skiing teaching action analysis and correction method program, and when the indoor skiing teaching action analysis and correction method program is executed by a processor, the method implements any one of the steps of the indoor skiing teaching action analysis and correction method described above.
In order to better explain the technical solution of the present invention, the following will describe in detail the specific steps of the indoor teaching action analysis and correction method through several embodiments.
Example 1
The student wears the training clothes that has sensing node and selects the double plate to train, and sensing node acquires student's posture data in real time, including: head node data, namely the vector of a straight line where the vertical axis of the head is located, double-arm node data, namely the vector of a straight line where the big arm and the small arm are located, trunk node data, namely the vector of a straight line where the upper body trunk is located, hip joint node data, namely the coordinate data of hip joints, thigh node data, namely the vector of a straight line where the thigh is located, and shank node data, namely the vector of a straight line where the shank is located, the included angle between the head and the horizontal direction, the bent angle between the two arms, the included angle between the trunk and the thighs and the bent angle between the knee joints are respectively calculated through the acquired posture data, specifically, the included angle between the head and the horizontal direction is calculated through the included angle between the vector of the straight line where the vertical axis of the head is positioned and the horizontal direction, the bending angle of the two arms is calculated through the linear vector of the big arm and the linear vector of the small arm, the included angle between the trunk and the thigh is calculated through the linear vector of the trunk of the upper body and the linear vector of the thigh, and the bending included angle of the knee joint is calculated through the linear vector of the thigh and the linear vector of the shank.
Searching out a corresponding reference action model according to the identity information of the student, such as age, height, gender and training grade, wherein the reference action model comprises a standard posture evaluation index, respectively calculating the deviation between the current posture evaluation index of the student and the standard posture evaluation index in the reference action model, if the deviation is greater than a preset threshold value, and if the deviation of the bending angle of the two arms is 30 degrees, prompting the student to correct the action through voice, and storing the action correction record in a learning situation file of the student.
In addition, in the training process of the student, a real-time video for trainee training is obtained through the video acquisition system, the frequency of swinging of the two arms of the student within 10 seconds is obtained through motion recognition, for example, if the frequency of swinging of the two arms of the primary student within 10 seconds is more than 7 times, the fact that the operation of the ski is too frequent indicates that the action of the ski is too frequent, the probability of the current high-speed ski state is high, and the student is prompted to keep the frequency of swinging of the two arms normally based on the fact that the current training level of the student is not suitable for high-speed skidding, so that the skidding speed. For example, if the number of times of trunk deflection of the primary student is more than 5 times in 10 seconds, the sliding state becomes frequent, and the safety risk exists for the primary student, and at this time, corrective action prompt can be performed.
Example 2
The trainees wear the training clothes. The training clothes are provided with the sensor of wireless sensing function outward, select the veneer board to train, and sensing node acquires student's posture data in real time, including: head node data, namely a vector of a straight line where a vertical axis of the head is located, double-arm node data, namely a straight line vector where a big arm and a small arm are located, trunk node data, namely a straight line vector where an upper body trunk is located, hip joint node data, namely coordinate data of a hip joint, thigh node data, namely a straight line vector where a thigh is located, and shank node data, namely a straight line vector where a shank is located, wherein an included angle between the head and the horizontal direction, a bent angle between the double arms, an included angle between the trunk and the thigh and a bent angle between the knee joint are respectively calculated through the obtained,
specifically, the included angle between the head and the horizontal direction is calculated through the included angle between the vector of the straight line where the vertical axis of the head is located and the horizontal direction, the bending angle of the two arms is calculated through the linear vector of the large arm and the linear vector of the small arm, the included angle between the trunk and the thigh is calculated through the linear vector of the trunk of the upper body and the linear vector of the thigh, and the bending included angle of the knee joint is calculated through the linear vector of the thigh and the linear vector of the shank.
Searching out a corresponding reference action model according to the identity information of the student, such as age, height, gender and training grade, wherein the reference action model comprises a standard posture evaluation index, respectively calculating the deviation between the current posture evaluation index of the student and the standard posture evaluation index in the reference action model, if the deviation is greater than a preset threshold value, and if the deviation of the bending angle of the two arms is 30 degrees, prompting the student to correct the action through voice, and storing the action correction record in a learning situation file of the student.
In addition, in the training process of the student, a real-time video for trainee training is obtained through the video acquisition system, the frequency of swinging of the two arms of the student within 10 seconds is obtained through motion recognition, for example, if the frequency of swinging of the two arms of the primary student within 10 seconds is more than 7 times, the fact that the operation of the ski is too frequent indicates that the action of the ski is too frequent, the probability of the current high-speed ski state is high, and the student is prompted to keep the frequency of swinging of the two arms normally based on the fact that the current training level of the student is not suitable for high-speed skidding, so that the skidding speed. For example, if the number of times of trunk deflection of the primary student is more than 5 times in 10 seconds, the sliding state becomes frequent, and the safety risk exists for the primary student, and at this time, corrective action prompt can be performed.
The invention discloses an indoor skiing teaching action analysis and correction method, a system and a readable storage medium.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An indoor skiing teaching action analysis and correction method is characterized by comprising the following steps:
acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data;
retrieving a preset reference action model according to the identity information and the training level of the student;
and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
2. The indoor skiing teaching action analysis and correction method according to claim 1, wherein the posture data comprises: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
3. The indoor skiing teaching action analysis and correction method according to claim 1, wherein the posture evaluation index comprises: the included angle between the head and the horizontal direction, the bent angle between the two arms, the included angle between the trunk and the thigh and the bent angle between the knee joint.
4. The indoor snow skiing teaching action analysis and correction method as claimed in claim 1, wherein the trainee identity information comprises: age, height, sex; the training grades are divided according to training duration acquired by the trainees, and specifically include: primary, intermediate, and advanced.
5. The indoor skiing teaching action analysis and correction method as claimed in claim 1, wherein the action correction comprises real-time action correction prompt information and trained learning condition training advice, the real-time action correction information is communicated in a voice mode, and the trained learning condition training advice comprises single action adjustment, single action training duration and coach one-to-one correction guidance.
6. The indoor snow skiing teaching action analysis and correction method as claimed in claim 1, wherein the preset reference action model comprises a single board training reference action model and a double board training reference action model.
7. The indoor skiing teaching action analysis and correction method as claimed in claim 1, wherein the physical evaluation index of the trainee is compared with a reference action model, the number of double-arm swinging and the number of trunk deflection of the trainee within a set time are obtained, the number of double-arm swinging and the number of trunk deflection of the trainee within the set time are respectively compared with a preset threshold, and action correction prompt is given if the number of double-arm swinging and the number of trunk deflection of the trainee within the set time are larger than the preset threshold.
8. An indoor skiing teaching action analysis and correction system is characterized by comprising a memory and a processor, wherein the memory comprises an indoor skiing teaching action analysis and correction method program, and the indoor skiing teaching action analysis and correction method program realizes the following steps when being executed by the processor:
acquiring physical state data of a student, and constructing a physical state evaluation index by using the physical state data;
retrieving a preset reference action model according to the identity information and the training level of the student;
and comparing the posture evaluation index of the student with the reference action model, if the deviation between the posture index of the student and the reference action model is greater than a preset value, performing action correction prompting, and simultaneously storing an action correction record to the learning situation file of the student.
9. The indoor snow skiing teaching action analysis and correction system as claimed in claim 8, wherein the posture data comprises: head node data, two-arm node data, trunk node data, hip joint node data, thigh node data and shank node data.
10. A computer-readable storage medium, wherein the computer-readable storage medium includes a program for an indoor snow skiing action analysis and correction method, and when the program for the indoor snow skiing action analysis and correction method is executed by a processor, the steps of the method for the indoor snow skiing action analysis and correction method according to any one of claims 1 to 7 are implemented.
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