Sports dance simulation training platform based on virtual reality technology

Abstract

The invention relates to the field of dance simulation training, and particularly discloses a sports dance simulation training platform based on virtual reality technology, comprising: a) a highly immersive VR environment generation module for creating a virtual dance scene including a dance pool, light, audience and background music, and continuously optimizing to provide a more real immersive experience; b) an intelligent motion capture and feedback system which integrates a high-precision sensor and an algorithm, can capture the dance motion of the trainee in real time, and feeds back a motion accuracy evaluation result through VR glasses in real time; according to the invention, through a highly immersive VR environment and platform, an approximately real dance scene is created for a trainer, including details such as a dance pool, light, audiences and background music, so that the trainer seems to be in a real dance environment, and training is more invested; the intelligent motion capture and feedback system can capture the dance motion of the trainee in real time and feed back the accuracy evaluation result of the motion through the VR glasses in real time.

Description

Technical Field

[0001] The invention belongs to the field of dance simulation training, in particular to a sports dance simulation training platform based on virtual reality technology. Background Art

[0002] Dance training refers to the teaching activities that comprehensively develop dancers' professional dance abilities by systematically rebuilding their body structure and functions and processing their movements and techniques based on scientific principles. It is not only a necessary way to cultivate dance talents, but also an important part of dance education.

[0003] Traditional dance training is often limited by environmental factors such as venue, lighting, and audience, making it difficult to simulate real stage effects. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a sports dance simulation training platform based on virtual reality technology to solve the problems in the prior art.

[0005] Sports dance simulation training platform based on virtual reality technology, including:

[0006] a) A highly immersive VR environment generation module, which is used to create virtual dance scenes, including dance floors, lighting, audiences, and background music, and is continuously optimized to provide a more realistic immersive experience;

[0007] b) Intelligent motion capture and feedback system, integrating high-precision sensors and algorithms, can capture the trainee's dance movements in real time and provide instant feedback of movement accuracy evaluation results through VR glasses;

[0008] c) Personalized training plan customization and AI-assisted module, which automatically generates and optimizes training plans based on the trainee's physical fitness, skill level and personal preferences, including dance courses of different difficulty levels. It also uses AI to analyze historical training data and intelligently recommend the most suitable training courses and progress;

[0009] d) Virtual coach and emotional interaction teaching module, providing a virtual coach with emotional interaction capabilities to guide trainees to complete dance moves, answer questions, and adjust teaching styles according to trainees' emotional responses to enhance learning experience and effectiveness;

[0010] e) Social competition and interactive function module, which allows trainers to connect with other users online to participate in virtual dance competitions and collaborative dance performances, making training more interesting and interactive, including a ranking system that records and displays trainers' scores and rankings in virtual dance competitions;

[0011] f) Data analysis and growth tracking system, which records and analyzes each training data of the trainee, including the degree of movement completion, progress speed and error correction, and scores the progress speed based on the movement accuracy evaluation results, adaptive difficulty adjustment mechanism and emotional interaction effect evaluation value.

[0012] Preferably, the action accuracy evaluation result is calculated by the following formula:

[0013]

[0014] Among them, A is the motion capture accuracy evaluation value, the value range is [0,1], the closer to 1, the higher the accuracy;

[0015] T is the training duration in seconds;

[0016] D(t) is the actual dance movement vector of the trainee at time t;

[0017] R(t) is the dance motion vector captured by the system at time t;

[0018] ||D(t)|| is the modulus of the actual dance motion vector at time t, i.e., the amplitude of the motion;

[0019] Var[D(t)] is the variance of the actual dance motion vector, which is used to measure the stability of the motion;

[0020] λ is the stability weight coefficient, which is adjusted according to the actual situation to balance the weight between accuracy and stability;

[0021] Range: A∈[0,1], where A=1 means that the system perfectly captures all the actions of the trainee, and A=0 means that the system fails to capture any valid actions.

[0022] Preferably, the personalized training difficulty adjustment formula is as follows:

[0023]

[0024] Wherein, Diff(n) is the personalized difficulty adjustment value at the nth training, which is used to adjust the training difficulty;

[0025] S i is the skill proficiency score during the i-th training, and its value range is [0, S max ];

[0026] S max is the maximum value of skill proficiency;

[0027] E i is the energy consumption value during the i-th training, and its value range is [0, +∞];

[0028] E base It is the basic physical energy consumption value, used to standardize physical energy consumption;

[0029] Err i is the number of errors during the i-th training;

[0030] Err max is the preset maximum error number threshold;

[0031] Range: Diff(n)∈[0,+∞), where higher values ​​indicate higher training difficulty and lower values ​​indicate lower training difficulty.

[0032] Preferably, the emotional interaction effect is evaluated by the following formula:

[0033]

[0034] Among them, E int is the emotional interaction effect evaluation value, with a value range of [0, 1]. The closer it is to 1, the better the emotional interaction effect.

[0035] F(t) is the emotional feedback vector of the trainer at time t;

[0036] E(t) is the emotional expression vector of the virtual coach at time t;

[0037] Neg(T) is the number of negative emotions that occurred in the trainee within T time;

[0038] Tot(T) is the total number of emotions that occurred in the trainee within time T;

[0039] Range: E int ∈[0,1], where E int =1 means the emotional interaction effect is perfect, E int =0 means that the emotional interaction is invalid.

[0040] Preferably, the data analysis and growth tracking system can generate a training report to provide the trainee with a detailed training summary, suggestions and improvement directions.

[0041] Preferably, the highly immersive VR environment generation module also includes an environmental adaptability adjustment function, which can dynamically adjust elements in the virtual dance scene, including dance floor layout, lighting effects, audience reaction and background music style, according to the trainee's preferences and training needs.

[0042] Preferably, the intelligent motion capture and feedback system also includes an error detection and correction module, which can automatically identify errors made by trainees in dance movements, and provide intuitive correction guidance through VR glasses to help trainees quickly correct erroneous movements.

[0043] Preferably, the personalized training plan customization module also includes a personalized challenge task setting function, which can design challenging dance tasks according to the trainee's skill level and progress speed, so as to stimulate the trainee's interest and motivation.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] Through a highly immersive VR environment, the platform creates a nearly realistic dance scene for trainees, including details such as the dance floor, lighting, audience, and background music, making trainees feel as if they are in a real dance environment, thus allowing them to be more engaged in training;

[0046] The intelligent motion capture and feedback system can capture the trainee's dance movements in real time and provide instant feedback on the accuracy of the movements through VR glasses. This instant feedback mechanism helps the trainee quickly identify and correct deficiencies in the movements, thereby improving training efficiency and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown:

[0050] Embodiment 1: The present invention provides a sports dance simulation training platform based on virtual reality technology, comprising:

[0051] a) A highly immersive VR environment generation module, which is used to create virtual dance scenes, including dance floors, lighting, audiences, and background music, and is continuously optimized to provide a more realistic immersive experience;

[0052] b) Intelligent motion capture and feedback system, integrating high-precision sensors and algorithms, can capture the trainee's dance movements in real time and provide instant feedback of movement accuracy evaluation results through VR glasses;

[0053] c) Personalized training plan customization and AI-assisted module, which automatically generates and optimizes training plans based on the trainee's physical fitness, skill level and personal preferences, including dance courses of different difficulty levels. It also uses AI to analyze historical training data and intelligently recommend the most suitable training courses and progress;

[0054] d) Virtual coach and emotional interaction teaching module, providing a virtual coach with emotional interaction capabilities to guide trainees to complete dance moves, answer questions, and adjust teaching styles according to trainees' emotional responses to enhance learning experience and effectiveness;

[0055] e) Social competition and interactive function module, which allows trainers to connect with other users online to participate in virtual dance competitions and collaborative dance performances, making training more interesting and interactive, including a ranking system that records and displays trainers' scores and rankings in virtual dance competitions;

[0056] f) Data analysis and growth tracking system, which records and analyzes each training data of the trainee, including the degree of movement completion, progress speed and error correction, and scores the progress speed based on the movement accuracy evaluation results, adaptive difficulty adjustment mechanism and emotional interaction effect evaluation value.

[0057] As can be seen from the above, this platform provides trainees with a virtual dance scene including a dance floor, lighting, audience and background music through a highly immersive VR environment, which greatly enhances the immersion and realism of training; the platform is also equipped with an intelligent motion capture and feedback system, which can accurately capture the trainees' dance movements and provide instant feedback on the accuracy of the movements, helping trainees to improve accurately; the personalized training plan customization and AI-assisted modules automatically generate and optimize training plans based on the trainees' physical fitness, skills and personal preferences, and realize intelligent teaching; in addition, the virtual coach and emotional interactive teaching module brings an emotionally rich virtual coach who can adjust teaching strategies according to the trainees' emotions and enhance the learning experience; the social competition and interactive function module allows trainees to compete and cooperate with other users online and enjoy the fun and interaction of training; finally, the data analysis and growth tracking system comprehensively records and analyzes training data to provide trainees with scientific progress evaluation and growth suggestions.

[0058] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that the action accuracy evaluation result is calculated by the following formula:

[0059]

[0060] Among them, A is the motion capture accuracy evaluation value, the value range is [0,1], the closer to 1, the higher the accuracy;

[0061] T is the training duration in seconds;

[0062] D(t) is the actual dance movement vector of the trainee at time t;

[0063] R(t) is the dance motion vector captured by the system at time t;

[0064] ||D(t)|| is the modulus of the actual dance motion vector at time t, i.e., the amplitude of the motion;

[0065] Var[D(t)] is the variance of the actual dance motion vector, which is used to measure the stability of the motion;

[0066] λ is the stability weight coefficient, which is adjusted according to the actual situation to balance the weight between accuracy and stability;

[0067] Range: A∈[0,1], where A=1 means the system perfectly captures all the actions of the trainee, and A=0 means the system fails to capture any valid actions;

[0068] The application process of the above formula is as follows:

[0069] Data Collection:

[0070] The high-precision sensors are used to capture the trainee's movement data in real time during the dance process, and a series of time series data are obtained, namely D(t) (actual dance movement vector) and R(t) (dance movement vector captured by the system).

[0071] At the same time, record the training time T.

[0072] Calculate motion capture accuracy:

[0073] For each time point t, the relative error between D(t) and R(t) is calculated, that is,

[0074] The above relative error is integrated over time to obtain the average relative error in the entire training process.

[0075] The stability weight coefficient λ is introduced to consider the stability of the action, namely Var[D(t)], and the average relative error is corrected.

[0076] Finally, the motion capture accuracy evaluation value A is obtained.

[0077] Feedback and Adjustment:

[0078] Based on the value of A, the trainee’s movement accuracy is instantly fed back through VR glasses.

[0079] If the A value is low, it means that there is a large deviation between the action captured by the system and the actual action, and the sensor position needs to be adjusted or the algorithm needs to be optimized.

[0080] If the A value is high, it means that the system captures the action more accurately, and the training difficulty or complexity can be further increased.

[0081] Specifically, the formula for adjusting the difficulty of personalized training is as follows:

[0082]

[0083] Wherein, Diff(n) is the personalized difficulty adjustment value at the nth training, which is used to adjust the training difficulty;

[0084] S i is the skill proficiency score during the i-th training, and its value range is [0, S max ];

[0085] S max is the maximum value of skill proficiency;

[0086] E i is the energy consumption value during the i-th training, and its value range is [0, +∞];

[0087] E base It is the basic physical energy consumption value, used to standardize physical energy consumption;

[0088] Err i is the number of errors during the i-th training;

[0089] Err max is the preset maximum error number threshold;

[0090] Range: Diff(n)∈[0,+∞), where higher values ​​indicate higher training difficulty and lower values ​​indicate lower training difficulty;

[0091] The application process of the above formula is as follows:

[0092] Data Collection:

[0093] Record the skill proficiency score S for each training i , physical energy consumption value E i , and the number of errors Err i .

[0094] Set the maximum value of skill proficiency S max and basic energy consumption value E base .

[0095] Preset maximum error count threshold Err max .

[0096] Calculate the personalized training difficulty adjustment value:

[0097] For each training session, calculate the relative value of the skill proficiency score

[0098] The logarithmic function of physical energy consumption value is introduced to reflect the impact of physical energy consumption on training difficulty.

[0099] Considering the adjustment of the number of errors to the difficulty of training, Make corrections.

[0100] The above factors are weighted and summed to obtain the personalized training difficulty adjustment value Diff(n).

[0101] Difficulty Adjustment:

[0102] The training difficulty is dynamically adjusted according to the value of Diff(n).

[0103] If Diff(n) is high, it means that the trainee’s current skill level is high, and the training difficulty can be increased, such as increasing the complexity of the movements or increasing the training time.

[0104] If Diff(n) is low, it means that the trainee’s current skill level is low and the training difficulty needs to be reduced, such as simplifying the movements or extending the rest time.

[0105] Specifically, the emotional interaction effect is evaluated by the following formula:

[0106]

[0107] Among them, E int is the emotional interaction effect evaluation value, with a value range of [0, 1]. The closer it is to 1, the better the emotional interaction effect.

[0108] F(t) is the emotional feedback vector of the trainer at time t;

[0109] E(t) is the emotional expression vector of the virtual coach at time t;

[0110] Neg(T) is the number of negative emotions that occurred in the trainee within T time;

[0111] Tot(T) is the total number of emotions that occurred in the trainee within time T;

[0112] Range: E int ∈[0,1], where E int =1 means the emotional interaction effect is perfect, E int =0 means emotional interaction is invalid:

[0113] The application process of the above formula is as follows:

[0114] Data Collection:

[0115] The trainer's emotional feedback vector F(t) and the virtual coach's emotional expression vector E(t) are captured in real time through emotion recognition technology.

[0116] Record the number of negative emotions Neg(T) and the total number of emotions Tot(T) that the trainees have during the training process.

[0117] Calculate the emotional interaction effect evaluation value:

[0118] For each time point t, calculate the similarity between F(t) and E(t), that is,

[0119] The above similarity is integrated over time to obtain the average similarity during the entire training process.

[0120] Introducing negative emotion regulators Correct the average similarity.

[0121] Finally, the emotional interaction effect evaluation value E is obtained int .

[0122] Feedback and Improvement:

[0123] According to E int The value of is used to evaluate the emotional interaction effect.

[0124] If E int A higher value indicates that the emotional interaction effect is better, and the complexity and depth of emotional interaction can be further increased.

[0125] If E int If it is low, it means that the emotional interaction effect is poor, and it is necessary to adjust the virtual coach's emotional expression strategy or increase the interactivity of emotional interaction.

[0126] From the above, we can see that the action accuracy assessment ensures the comprehensiveness and accuracy of the assessment results by comprehensively considering the accuracy of motion capture, training duration, movement amplitude, stability and their weights; the personalized training difficulty adjustment dynamically adjusts the training difficulty based on factors such as skill proficiency, physical energy consumption and number of errors to meet the personalized needs of different trainees; at the same time, the emotional interaction effect evaluation comprehensively evaluates the effectiveness of emotional interaction by comparing the emotional feedback and expression of trainees and virtual coaches, combined with the frequency of negative emotions; the introduction of these quantitative formulas not only improves the intelligence level of the training platform, but also provides trainees with more scientific and accurate training feedback and personalized guidance, further enhancing the training effect and experience.

[0127] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that the data analysis and growth tracking system can generate a training report to provide the trainee with a detailed training summary, suggestions and improvement directions.

[0128] Specifically, the highly immersive VR environment generation module also includes an environmental adaptability adjustment function, which can dynamically adjust elements in the virtual dance scene, including dance floor layout, lighting effects, audience reaction and background music style, according to the trainee's preferences and training needs.

[0129] Specifically, the intelligent motion capture and feedback system also includes an error detection and correction module, which can automatically identify errors made by trainees in dance movements, and provide intuitive correction guidance through VR glasses to help trainees quickly correct erroneous movements.

[0130] Specifically, the personalized training plan customization module also includes a personalized challenge task setting function, which can design challenging dance tasks according to the trainees' skill level and progress rate to stimulate the trainees' interest and motivation.

[0131] As can be seen from the above, this embodiment further enhances the functions of the data analysis and growth tracking system, and can automatically generate detailed training reports to provide trainees with personalized training summaries, improvement suggestions and future development directions; in addition, the highly immersive VR environment generation module has added an environmental adaptability adjustment function, which can flexibly adjust various elements in the virtual dance scene according to the trainee's preferences and needs, such as dance floor layout, lighting, audience reaction and background music, thereby enhancing the personalization and immersion of training; the intelligent motion capture and feedback system has added an error detection and correction module, which can instantly identify and correct the trainee's dance movement errors and accelerate skill improvement; at the same time, the personalized training plan customization module has added a challenge task setting function, which designs challenging tasks according to the trainee's actual situation, effectively stimulating the trainee's interest and participation.

[0132] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0133] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0134] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0135] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0137] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0138] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A sports dance simulation training platform based on virtual reality technology, characterized in that: include: a) A highly immersive VR environment generation module, which is used to create virtual dance scenes, including dance floors, lighting, audiences, and background music, and is continuously optimized to provide a more realistic immersive experience; b) Intelligent motion capture and feedback system, integrating high-precision sensors and algorithms, can capture the trainee's dance movements in real time and provide instant feedback of movement accuracy evaluation results through VR glasses; c) Personalized training plan customization and AI-assisted module, which automatically generates and optimizes training plans based on the trainee's physical fitness, skill level and personal preferences, including dance courses of different difficulty levels. It also uses AI to analyze historical training data and intelligently recommend the most suitable training courses and progress; d) Virtual coach and emotional interaction teaching module, providing a virtual coach with emotional interaction capabilities to guide trainees to complete dance moves, answer questions, and adjust teaching styles according to trainees' emotional responses to enhance learning experience and effectiveness; e) Social competition and interactive function module, which allows trainers to connect with other users online to participate in virtual dance competitions and collaborative dance performances, making training more interesting and interactive, including a ranking system that records and displays trainers' scores and rankings in virtual dance competitions; f) Data analysis and growth tracking system, which records and analyzes each training data of the trainee, including the degree of movement completion, progress speed and error correction, and scores the progress speed based on the movement accuracy evaluation results, adaptive difficulty adjustment mechanism and emotional interaction effect evaluation value.

2. The sports dance simulation training platform based on virtual reality technology as claimed in claim 1, characterized in that: The action accuracy evaluation result is calculated by the following formula: Among them, A is the motion capture accuracy evaluation value, the value range is [0,1], the closer to 1, the higher the accuracy; T is the training duration in seconds; D(t) is the actual dance movement vector of the trainee at time t; R(t) is the dance motion vector captured by the system at time t; ||D(t)|| is the modulus of the actual dance motion vector at time t, i.e., the amplitude of the motion; Var[D(t)] is the variance of the actual dance motion vector, which is used to measure the stability of the motion; λ is the stability weight coefficient, which is adjusted according to the actual situation to balance the weight between accuracy and stability; Range: A∈[0,1], where A=1 means that the system perfectly captures all the actions of the trainee, and A=0 means that the system fails to capture any valid actions.

3. The sports dance simulation training platform based on virtual reality technology as claimed in claim 2, characterized in that: The personalized training difficulty adjustment formula is as follows: Wherein, Diff(n) is the personalized difficulty adjustment value at the nth training, which is used to adjust the training difficulty; S i is the skill proficiency score during the i-th training, and its value range is [0, S max ]; S max is the maximum value of skill proficiency; E i is the energy consumption value during the i-th training, and its value range is [0, +∞]; E base It is the basic physical energy consumption value, used to standardize physical energy consumption; Err i is the number of errors during the i-th training; Err max is the preset maximum error number threshold; Range: Diff(n)∈[0,+∞), where higher values ​​indicate higher training difficulty and lower values ​​indicate lower training difficulty.

4. The sports dance simulation training platform based on virtual reality technology as claimed in claim 3, characterized in that: The emotional interaction effect is evaluated by the following formula: Among them, E int is the emotional interaction effect evaluation value, with a value range of [0, 1]. The closer it is to 1, the better the emotional interaction effect. F(t) is the emotional feedback vector of the trainer at time t; E(t) is the emotional expression vector of the virtual coach at time t; Neg(T) is the number of negative emotions that occurred in the trainee within T time; Tot(T) is the total number of emotions that occurred in the trainee within time T; Range: E int ∈[0,1], where E int =1 means the emotional interaction effect is perfect, E int =0 means that the emotional interaction is invalid.

5. The sports dance simulation training platform based on virtual reality technology as claimed in claim 4, characterized in that: The data analysis and growth tracking system can generate training reports to provide trainers with detailed training summaries, suggestions and improvement directions.

6. The sports dance simulation training platform based on virtual reality technology as claimed in claim 5, characterized in that: The highly immersive VR environment generation module also includes an environmental adaptability adjustment function, which can dynamically adjust elements in the virtual dance scene, including dance floor layout, lighting effects, audience reactions and background music style, according to the trainee's preferences and training needs.

7. The sports dance simulation training platform based on virtual reality technology as claimed in claim 6, characterized in that: The intelligent motion capture and feedback system also includes an error detection and correction module, which can automatically identify errors made by trainees in dance movements and provide intuitive correction guidance through VR glasses to help trainees quickly correct erroneous movements.

8. The sports dance simulation training platform based on virtual reality technology as claimed in claim 7, characterized in that: The personalized training plan customization module also includes a personalized challenge task setting function, which can design challenging dance tasks according to the trainee's skill level and progress speed to stimulate the trainee's interest and motivation.

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