CN111785347A - Fitness recommendation system and method based on motion record - Google Patents
Fitness recommendation system and method based on motion record Download PDFInfo
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Abstract
The invention relates to the technical field of sports and fitness, in particular to a fitness recommendation system and method based on a sports record, wherein the method is applied to the system, and the system comprises an information collection module: the position information used for obtaining the user to exercise; the system is also used for acquiring exercise history records or body preliminary evaluation information of the user; a plan generation module: the training plan is used for generating a plurality of sets of different attributes; a plan sending module: the training system is used for recommending a plurality of training plans to a user and receiving the training plan selected by the user; a plan completion recording module: exercise data for recording a user performing an exercise according to a training plan; a plan optimization module: and the training plan is used for analyzing, learning and optimizing the recommended training plan according to the exercise data and is recommended to the user next time. The invention can solve the problems that the user is lack of fitness experience or professional fitness knowledge training, and is easy to be unsafe and poor in effect during fitness.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a fitness recommendation system and method based on a motion record.
Background
With the continuous development and progress of society, the living standard of people is gradually improved, the economic capability and the consumption consciousness are also gradually improved, so that the spiritual entertainment and the physical health are more and more valued by people. Wherein, in the aspect of healthy, the crowd who steps into the motion body-building ranks is also more and more, and all kinds of body-building products and gymnasiums also gradually spread throughout each corner in city, if: treadmills, dumbbells, exercise wheels, pull ropes and the like are common indoor fitness equipment. According to investigation, although the fitness equipment can meet the fitness requirements of people to a certain extent, some users without fitness experience are difficult to standardize the fitness equipment under the condition of no professional guidance of fitness coaches, and a targeted fitness plan is difficult to make according to self-training conditions and targets. That is to say, if the user who lacks the fitness experience or the professional fitness knowledge training is trained blindly, the safety and the effectiveness of the training cannot be ensured.
Disclosure of Invention
One of the main objects of the present invention is to provide a fitness recommendation system based on exercise history, which can automatically recommend a more appropriate exercise plan to a user when the same part is selected again for exercise.
In order to achieve the above object, the present invention provides a fitness recommendation system based on exercise records, comprising a server, wherein the server comprises the following modules:
an information collection module: the position information used for obtaining the user to exercise; the system is also used for acquiring exercise history records and body preliminary evaluation information of the user;
a plan generation module: the training plan generating system is used for generating a plurality of sets of training plans with different attributes according to the part information, the exercise history record and the body preliminary evaluation information;
a plan sending module: the training system is used for recommending a plurality of training plans to a user and receiving the training plan selected by the user;
a plan completion recording module: exercise data for recording a user performing an exercise according to a training plan;
a plan optimization module: and the training data analysis module is used for analyzing the recommended training plan according to the exercise data, optimizing the recommended training plan according to the analysis result and recommending the training plan to the user next time.
The working principle and the advantages of the invention are as follows:
1. the information collection module is arranged, so that a user can conveniently know which position of the body the user wants to exercise, the plan generation module can generate a proper training plan conveniently, the plan generation module can generate the training plan suitable for the user according to the part information, the exercise history record and the body preliminary evaluation information, the user only needs to exercise pertinently by executing the training plan, and the training safety and effectiveness are guaranteed.
2. Due to the fact that the physical conditions of each person are different and the exercise consciousness is different, the same training plan can have different effects for different persons, the plan optimization module can optimize exercise data collected by the plan completion degree recording module, and when the user selects the same part again for exercise next time, the user can recommend a more appropriate exercise plan to the user, and the exercise effect is improved.
Further, the system also comprises the following modules:
an action collection module: the exercise device is used for collecting a plurality of exercise actions and storing the exercise actions in a preset action database.
The exercise positions are different, so that the body building actions are different, and the hand collection of the body building actions is convenient for making an exercise plan.
Further, the system also comprises the following modules:
the action attribute editing module: the exercise device is used for editing the action attributes of the exercise action, wherein the action attributes comprise an action difficulty level, an action intensity level, an exercise part and an exercise effect; the attributes of the training plan include training intensity, training difficulty and training effect, the training intensity includes the number of the body-building actions, and the training difficulty includes the type of the body-building actions.
The body-building action has attributes such as difficulty level, action intensity level, body-building part and body-building effect, and a proper body-building action is selected conveniently when an exercise plan is made in an attribute editing mode.
Further, the system also comprises the following modules:
an action association module: the fitness equipment is used for performing association classification on a plurality of fitness actions, and factors of the association classification comprise fitness effects of each fitness action;
an action marking module: for marking the motion relations among the associated fitness activities, the motion relations including superior substitution, inferior substitution, and superior substitution.
Because the same effect may exist among the body-building actions, when the exercise plan is made, the searching and the selecting of the proper body-building action are troublesome, and the proper body-building action is conveniently and quickly selected from the action database to carry out the replacement of the upper-level action, the replacement of the lower-level action and the replacement of the horizontal-level action through the action association and the action mark, so that the proper exercise plan is conveniently and quickly made.
Further, the number of the training plans recommended to the user is at least two.
The user can conveniently make a selection according to the self requirement.
Further, the exercise data includes a plan completion, a physical load condition, and a plan completion experience; the plan completion degree comprises the completion quantity and the completion quality of the body-building action, and the plan completion feeling comprises the strength feeling and the strength feeling of the body-building action.
The exercise results actually obtained by the user according to the exercise program are conveniently known through the collection of exercise data, so that the optimization of the subsequent exercise program is facilitated.
Further, the plan optimization module comprises the following sub-modules:
a plan scoring submodule: the system comprises a training program module, a training data analysis module and a recommendation module, wherein the training program module is used for analyzing training data of a training program according to training data and preset grading rules; the scoring rule is as follows: matching different scores according to the completion quality of each body-building action, and calculating the average score of each body-building action; the average score is then labeled as the score of the training program.
Because the number of the fitness actions in each training plan may be different, the way of calculating the total score is not suitable, and the real evaluation of the user on the training plan can be conveniently quantified through the way of average score.
Further, the training plan generated by the plan generation module, the training plan optimized by the plan optimization module, and the scores corresponding to the training plan are stored in a plan database, and the plan generation module includes the following sub-modules:
a plan recommendation sub-module: the system is used for screening other users similar or similar to the user condition, obtaining a rating set obtained by the user and other users through a training plan for many times, obtaining a recommended rating from the rating set according to a test weight proportion, selecting a training plan with the same rating from a plan database according to the recommended rating, and sending the training plan to a plan generating module.
Other users with similar conditions are added, so that the recommendation range of the exercise plan is expanded.
Further, the plan optimization module comprises the following sub-modules:
training plan labeling submodule: and when the score of the training plan is lower than the last N scores and the score of the training plan does not exceed the critical threshold of the average score of the N scores, marking the training plan as a failed plan and reducing the recommendation priority of the training plan.
If the multiple scores are too low and are lower than the critical threshold of the average score, the establishment of the training plan is failed, and the subsequent establishment of the exercise plan can be referred through marking.
Drawings
FIG. 1 is a logic block diagram of an embodiment of the exercise record-based fitness recommendation system of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
The exercise record-based fitness recommendation system, substantially as shown in fig. 1, includes a server and a user side.
The user side is used for the user to select the body part of wanting to take exercise, and the body part includes biceps brachii muscle, pectoralis muscle, foreback, middle back, lower back, neck triceps brachii muscle group, shank muscle group, trapezius muscle, shoulder, abdominal muscle, buttock muscle group, hip psoas muscle, quadriceps femoris muscle, hamstring muscle, adductor muscle group, abductor muscle group and the like. For example, the user can select a small sheet through the user terminal, and the small sheet is sent to the server after the selection is completed.
The server comprises a database and an action database, and specifically comprises the following modules:
an information collection module: the part information used for accepting the user at the user end to exercise; the system is also used for acquiring exercise history records and body preliminary evaluation information of the user from the database; the user who takes exercise for the first time generally does not have the exercise history record, only has the preliminary evaluation information of health, and the preliminary evaluation information of health includes some simple action tests of user, for example push-up, roll up the abdomen, position of sitting body tiptoe, dull and stereotyped support, lean on the wall to squat deeply etc.. While a user who has undergone multiple exercises will maintain an exercise history of the relevant exercise data in the database.
A plan generation module: the training plan generating system is used for generating a plurality of sets of training plans with different attributes according to the part information, the exercise history record and the body preliminary evaluation information.
When the plan generation module generates the training plan, the following modules in the server are executed:
an action collection module: the exercise device is used for collecting a plurality of exercise actions and storing the exercise actions in a preset action database.
The action attribute editing module: the action attributes are used for editing the exercise action and comprise an action difficulty level, an action intensity level, an exercise part and an exercise effect; the attributes of the training plan include training intensity, training difficulty, and training effect, the training intensity includes the number of fitness actions, and the training difficulty includes the type of fitness actions.
An action association module: the fitness equipment is used for performing association classification on a plurality of fitness actions, and factors of the association classification comprise fitness effects of each fitness action;
an action marking module: for marking the motion relations among the associated fitness activities, the motion relations including superior substitution, inferior substitution, and superior substitution.
The number of training plans recommended to the user by the plan generating module is at least two; in this embodiment, three sets are preferred, for example, the small body part to be exercised includes two body parts, i.e. pectoral muscle and shoulder, and the local contents of the generated three schemes are shown in table 1 below:
TABLE 1
A plan sending module: the system is used for recommending the three sets of training plans to a user and receiving the training plan selected by the user; the user receives three sets of training plans through the user terminal, selects one set of training plan to perform actual exercise, and feeds back the selected set of training plan to the server.
A plan completion recording module: exercise data for recording a user performing an exercise according to a training plan; the exercise data comprises plan completion, physical load condition and plan completion feeling; the plan completion degree comprises the completion quantity (group number) and completion quality of the body-building action, and the plan completion feeling comprises the strength feeling and the strength feeling of the body-building action. Wherein the acquisition of exercise data can be performed by a user through a user terminal. The local content of the exercise data is shown in table 2 below:
TABLE 2
A plan optimization module: and the training data analysis module is used for analyzing the recommended training plan according to the exercise data, performing learning optimization on the recommended training plan according to the analysis result, and recommending the training plan to the user next time.
The plan optimization module comprises the following sub-modules:
a plan scoring submodule: the system comprises a training program module, a training data analysis module and a recommendation module, wherein the training program module is used for analyzing training data of a training program according to training data and preset grading rules; the scoring rule is as follows: matching different scores according to the completion quality of each body-building action, and calculating the average score of each body-building action; the average score is then labeled as the score of the training program. The completion quality (completion degree) of each body-building action is divided into five grades, namely poor, normal, good and good, the corresponding scores are 1-5, the average score of each body-building action is calculated, and the average score is the score of the training plan.
The training plan generated by the plan generation module, the training plan optimized by the plan optimization module and the scores corresponding to the training plan are stored in a plan database, and the plan generation module comprises the following sub-modules:
a plan recommendation sub-module: the system is used for screening other users similar or similar to the user condition, obtaining a rating set obtained by the user and other users through a training plan for many times, obtaining a recommended rating from the rating set according to a test weight proportion, selecting a training plan with the same rating from a plan database according to the recommended rating, and sending the training plan to a plan generating module. In this embodiment, the test weight ratio is 7:3, which means that the scores of training plans of the user in the last 7 times are collected, and the scores of training plans of other users with similar or similar exercise conditions are collected in 3 times.
Training plan labeling submodule: and when the score of the training plan is lower than the last N scores and the score of the training plan does not exceed the critical threshold of the average score of the N scores, marking the training plan as a failed plan and reducing the recommendation priority of the training plan. In this embodiment, N is 10, and the critical threshold is 30%. For example, if a selected exercise program is a little exercise program that has been completed by the person, and the actual score for the current exercise program is less than the score for the previous 10 exercise programs and less than 30% of the average score for the 10 exercise programs, then the exercise program will be flagged as a failed program.
The fitness recommendation method based on the exercise record comprises the following steps of:
an information collection step: receiving part information of a user at a user end, which the user wants to exercise; the system is also used for acquiring exercise history records or body preliminary evaluation information of the user from the database; the user who takes exercise for the first time generally does not have the exercise history record, only has the preliminary evaluation information of health, and the preliminary evaluation information of health includes some simple action tests of user, for example push-up, roll up the abdomen, position of sitting body tiptoe, dull and stereotyped support, lean on the wall to squat deeply etc.. While a user who has undergone multiple exercises will maintain an exercise history of the relevant exercise data in the database.
A plan generating step: and generating a plurality of sets of training plans with different attributes according to the part information, the exercise history record and the body preliminary evaluation information.
The plan generating step further includes, when generating the training plan, the steps of:
an action collection step: a plurality of body-building actions are collected and stored in a preset action database.
And an action attribute editing step: editing action attributes of the body-building action, wherein the action attributes comprise an action difficulty level, an action intensity level, a body-building part and a body-building effect; the attributes of the training plan include training intensity, training difficulty, and training effect, the training intensity includes the number of fitness actions, and the training difficulty includes the type of fitness actions.
And an action association step: performing association classification on a plurality of fitness actions, wherein the factors of the association classification comprise the fitness effect of each fitness action;
an action marking step: the markers correlate motion relationships between the workout motions, including superior substitution, inferior substitution, and superior substitution.
At least two sets of training plans are recommended to the user in the plan generating step; three sets are preferred in this embodiment.
A plan sending step: recommending the three sets of training plans to the user and receiving the training plan selected by the user; the user receives three sets of training plans through the user terminal, selects one set of training plan to perform actual exercise, and feeds back the selected set of training plan to the server.
And a plan completion degree recording step: recording exercise data of a user exercising according to a training plan; the exercise data comprises plan completion, physical load condition and plan completion feeling; the plan completion degree comprises the completion quantity (group number) and completion quality of the body-building action, and the plan completion feeling comprises the strength feeling and the strength feeling of the body-building action. The exercise data can be collected by the user through the user side and then fed back to the server through the user side.
Plan optimization step: and analyzing the recommended training plan according to the exercise data, performing learning optimization on the recommended training plan according to the analysis result, and recommending the training plan to the user next time.
The plan optimization step comprises the following substeps:
a plan scoring substep: scoring the recommended training plan according to exercise data and a preset scoring rule during analysis; the scoring rule is as follows: matching different scores according to the completion quality of each body-building action, and calculating the average score of each body-building action; the average score is then labeled as the score of the training program. The finishing quality of each body-building action is divided into five grades, namely poor, normal, good and good, the corresponding scores are 1-5, the average score of each body-building action is calculated, and the average score is the score of the training plan.
The training plan generated in the plan generating step, the training plan optimized by the plan optimizing module and the scores corresponding to the training plan are stored in a plan database, and the plan generating step comprises the following substeps:
plan recommendation substep: and screening other users similar or similar to the user condition, acquiring a score set obtained by the user and other users through the training plan for multiple times, acquiring a recommended score from the score set according to the test weight proportion, selecting the training plan with the same score from a plan database according to the recommended score, and sending the training plan to a plan generating module. In this embodiment, the test weight ratio is 7:3, which means that the scores of training plans of the user in the last 7 times are collected, and the scores of training plans of other users with similar or similar conditions are collected in 3 times.
The plan optimization step further comprises the following substeps:
training plan marking sub-step: and when the score of the training plan is lower than the last N scores and the score of the training plan does not exceed the critical threshold of the average score of the N scores, marking the training plan as a failed plan and reducing the recommendation priority of the training plan. In this embodiment, N is 10, and the critical threshold is 30%.
Example two
In this embodiment, because generally, for office workers, the number of times of going to the gym for exercise is limited every week, and may be only 2-3 times a week, and such intermittent exercise usually does not achieve a better exercise effect, if a fitness user goes to the gym every day for high-intensity exercise (especially for exercise with personal education, the intensity is usually larger), the exercise amount may be too large and overload may occur to the body of the user, and the effect is not very good. The user side further comprises a home training application module for the fitness user to apply for home training to the server, the home training application in the embodiment only comprises the time except for the training performed in fitness places such as a gym every week, if the intermediate exercise is not continuous, a better exercise effect is achieved, and the user side can also perform proper exercise at home when not going to the gym. And after receiving the training application at home, the server automatically matches whether the fitness user has a corresponding personal coach, automatically establishes a private education instruction group if the fitness user has the corresponding personal coach, and simultaneously invites the personal coach corresponding to the fitness user to enter the private education instruction group. In this embodiment, based on that a general fitness user has a personal trainer online, and the personal trainer is most aware of the general training, physical conditions, training plan and training effects of the fitness user at ordinary times, when the fitness user actively applies for training at home, the personal trainer is directly matched with the fitness user preferentially, compared with the fitness user who directly searches network resources blindly when training at home, for example, if some training videos on the network are followed, the personal trainer knows all the conditions of the fitness user, and gives the training plan and training actions of training at home according to the current specific conditions of the fitness user, for example, the training intensity is not too high when training at home, so that only a few actions are given to achieve the purpose of training which part of the body of the fitness user in a period of time, and according to the requirements of the fitness user and the corresponding plan, after the fitness user performs the first set of actions appointed for a period of time, the second set of actions for exercising for the next period of time can be given in a targeted manner, so that the fitness at home is more scientific, more reasonable and more targeted, and the optimal fitness effect is achieved.
After a private education guide group is established, a fitness user transfers private education guide basic funds to a server, after a private coach uploads the home fitness plan and the introduction of fitness actions of the fitness user in a plan time period on the private education guide group, the server transfers the private education guide basic funds to the private coach, the private education guide basic funds are less than the normal private education teaching amount, after fitness begins, the fitness user pays a fitness guarantee fund to the server, the fitness guarantee fund guarantees that the fitness user can perform fitness in a specified time period, if the fitness user does not perform fitness or has poor fitness effect and the fitness actions are not standard in the specified time period, part of the fitness guarantee funds are transferred to the fitness user, and the rest of the fitness guarantee funds are transferred to the private coach as rewards. If the fitness users insist on fitness exercise within the specified time period, and the action is standard and the effect is good, the fitness guarantee fund is classified as a private coach. For example, if the time period of the fitness plan made by the personal education for the fitness user is one month, the fitness user can pay the fitness guarantee fund to the server every time, the four times of fitness guarantee fund plus the basic fund for the personal education is more than the amount of the general personal education, which is equivalent to paying the reward of the personal trainer in batches, but the reward is paid in batches and is more than the amount of the general personal education, so that the personal trainer is prompted to take charge more seriously to instruct the fitness user to build the fitness, and the high reward is obtained, and the fitness user can be prompted to insist on exercising and not to give up the exercise easily.
The personal coach regularly pushes the action video to the private education guide group, the fitness user regularly pushes the recorded video of the fitness player to the private education guide group, the server automatically analyzes the action difference between the action video and the recorded video, and when the similarity of the fitness actions contained in the action video and the recorded video reaches more than 70%, the fitness action standard of the fitness user is determined. If the similarity of the fitness actions contained in the recorded video of the fitness user and the motion video of the personal trainer still does not reach 70% after one week, the server sends a strengthening guidance prompt to the personal trainer and automatically establishes a video guidance picture for the fitness user and the personal trainer, and if the similarity of the fitness actions contained in the recorded video of the fitness user and the motion video of the personal trainer again does not reach 70% after the video guidance, the difficulty of the set of actions is considered to be too large, the exercise effect of the fitness user is not good, part of fitness guarantee money is deducted, and the part of the fitness guarantee money is classified to the fitness user to promote the personal trainer to strengthen the guidance or change the fitness plan.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. Exercise record-based fitness recommendation system, characterized in that: the server comprises the following modules:
an information collection module: the position information used for obtaining the user to exercise; the system is also used for acquiring exercise history records and body preliminary evaluation information of the user;
a plan generation module: the training plan generating system is used for generating a plurality of sets of training plans with different attributes according to the part information, the exercise history record and the body preliminary evaluation information;
a plan sending module: the training system is used for recommending a plurality of training plans to a user and receiving the training plan selected by the user;
a plan completion recording module: exercise data for recording a user performing an exercise according to a training plan;
a plan optimization module: and the training data analysis module is used for analyzing the recommended training plan according to the exercise data, optimizing the recommended training plan according to the analysis result and recommending the training plan to the user next time.
2. The exercise record-based workout recommendation system of claim 1, wherein: the system also comprises the following modules:
an action collection module: the exercise device is used for collecting a plurality of exercise actions and storing the exercise actions in a preset action database.
3. The exercise record-based workout recommendation system of claim 2, wherein: the system also comprises the following modules:
the action attribute editing module: the exercise device is used for editing the action attributes of the exercise action, wherein the action attributes comprise an action difficulty level, an action intensity level, an exercise part and an exercise effect; the attributes of the training plan include training intensity, training difficulty and training effect, the training intensity includes the number of the body-building actions, and the training difficulty includes the type of the body-building actions.
4. A workout recommendation system based on exercise records according to claim 3, characterized in that: the system also comprises the following modules:
an action association module: the fitness equipment is used for performing association classification on a plurality of fitness actions, and factors of the association classification comprise fitness effects of each fitness action;
an action marking module: for marking the motion relations among the associated fitness activities, the motion relations including superior substitution, inferior substitution, and superior substitution.
5. The exercise record-based workout recommendation system of claim 1, wherein: at least two sets of training plans are recommended to the user.
6. The exercise record-based workout recommendation system of claim 1, wherein: the exercise data comprises plan completion, physical load condition and plan completion feeling; the plan completion degree comprises the completion quantity and the completion quality of the body-building action, and the plan completion feeling comprises the strength feeling and the strength feeling of the body-building action.
7. The exercise record-based workout recommendation system of claim 6, wherein: the plan optimization module comprises the following sub-modules:
a plan scoring submodule: the system comprises a training program module, a training data analysis module and a recommendation module, wherein the training program module is used for analyzing training data of a training program according to training data and preset grading rules; the scoring rule is as follows: matching different scores according to the completion quality of each body-building action, and calculating the average score of each body-building action; the average score is then labeled as the score of the training program.
8. A workout recommendation system based on exercise records according to claim 7, characterized in that: the training plan generated by the plan generation module, the training plan optimized by the plan optimization module and the scores corresponding to the training plan are stored in a plan database, and the plan generation module comprises the following sub-modules:
a plan recommendation sub-module: the system is used for screening other users similar or similar to the user condition, obtaining a rating set obtained by the user and other users through a training plan for many times, obtaining a recommended rating from the rating set according to a test weight proportion, selecting a training plan with the same rating from a plan database according to the recommended rating, and sending the training plan to a plan generating module.
9. A workout recommendation system based on exercise records according to claim 8, characterized in that: the plan optimization module comprises the following sub-modules:
training plan labeling submodule: and when the score of the training plan is lower than the last N scores and the score of the training plan does not exceed the critical threshold of the average score of the N scores, marking the training plan as a failed plan and reducing the recommendation priority of the training plan.
10. Exercise record based fitness recommendation method, characterized by being implemented by the exercise record based fitness recommendation system of any one of claims 1 to 9.
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| CN113299365A (en) * | 2021-05-18 | 2021-08-24 | 中金育能教育科技集团有限公司 | Physical training plan generation method and device and electronic equipment |
| CN113656640A (en) * | 2021-08-23 | 2021-11-16 | 成都拟合未来科技有限公司 | Fitness training method, system, device and medium |
| CN114400065A (en) * | 2021-12-17 | 2022-04-26 | 重庆特斯联智慧科技股份有限公司 | Gymnasium robot system assisting in exercise recovery and control method thereof |
| CN115798676A (en) * | 2022-11-04 | 2023-03-14 | 中永(广东)网络科技有限公司 | Interactive experience analysis management method and system based on VR technology |
| WO2023173590A1 (en) * | 2022-03-14 | 2023-09-21 | 成都拟合未来科技有限公司 | Training plan generation method and system, apparatus, and medium |
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| CN113656640A (en) * | 2021-08-23 | 2021-11-16 | 成都拟合未来科技有限公司 | Fitness training method, system, device and medium |
| CN113656640B (en) * | 2021-08-23 | 2024-04-30 | 成都拟合未来科技有限公司 | Body-building training method, system and device and medium |
| CN114400065A (en) * | 2021-12-17 | 2022-04-26 | 重庆特斯联智慧科技股份有限公司 | Gymnasium robot system assisting in exercise recovery and control method thereof |
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| CN115798676A (en) * | 2022-11-04 | 2023-03-14 | 中永(广东)网络科技有限公司 | Interactive experience analysis management method and system based on VR technology |
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