Fitness plan recommendation method and apparatus, storage medium, and program product

Abstract

The present disclosure relates to the technical field of computers, and relates to a fitness plan recommendation method and apparatus, a storage medium, and a program product. The fitness plan recommendation method comprises: in response to receiving request information of a user for acquiring a fitness plan, generating a first fitness plan corresponding to a target time period, wherein the target time period comprises a plurality of sub-time periods, and the first fitness plan comprises exercise types and trained body parts in the plurality of sub-time periods; on the basis of the first fitness plan, generating a plurality of second fitness plans corresponding to the plurality of sub-time periods, wherein the plurality of second fitness plans comprise names and fitness guidance content of a plurality of target fitness movements corresponding to the exercise types and trained body parts in the plurality of sub-time periods in the first fitness plan; and displaying the first fitness plan and the plurality of second fitness plans to the user. The present disclosure can improve the accuracy of fitness plan recommendation.

Description

Fitness plan recommendation method and device, storage medium, and program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computers, and particularly relates to a fitness plan recommendation method and device, a storage medium, and a program product. BACKGROUND

[0002] With the development of artificial intelligence technology, the application of intelligent agents has penetrated into various aspects of our life, such as intelligent question answering, intelligent voice assistants, intelligent planning, and the like.

[0003] In the related art, a multi-day fitness plan is directly generated in one step according to a request of a user to obtain a fitness plan.

[0004] SUMMARY

[0005] This summary is provided to introduce a selection of concepts, which will be described in greater detail below in the detailed description section. This summary does not necessarily identify key or essential features of the claimed subject matter and is not intended to limit the scope of the claimed subject matter.

[0006] According to a first aspect of some embodiments of the present disclosure, a fitness plan recommendation method is provided, including:

[0007] In response to receiving request information of a user to obtain a fitness plan, a first fitness plan corresponding to a target time period is generated, wherein the target time period includes a plurality of sub-time periods, and the first fitness plan includes exercise types and exercise parts of the plurality of sub-time periods;

[0008] According to the first fitness plan, a plurality of second fitness plans corresponding to the plurality of sub-time periods are generated, wherein the plurality of second fitness plans include names and fitness guidance contents of a plurality of target fitness actions corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan;

[0009] The first fitness plan and the plurality of second fitness plans are displayed to the user.

[0010] According to a second aspect of some embodiments of the present disclosure, a fitness plan recommendation device is provided, including:

[0011] A first generation module is configured to, in response to receiving request information of a user to obtain a fitness plan, generate a first fitness plan corresponding to a target time period, wherein the target time period includes a plurality of sub-time periods, and the first fitness plan includes exercise types and exercise parts of the plurality of sub-time periods;

[0012] a second generating module configured to generate, according to the first fitness plan, a plurality of second fitness plans corresponding to the plurality of sub-time periods, wherein the plurality of second fitness plans comprise names and fitness guidance contents of a plurality of target fitness actions corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan;

[0013] a displaying module configured to display the first fitness plan and the plurality of second fitness plans to the user.

[0014] According to a third aspect of some embodiments of the present disclosure, an electronic device is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute a fitness plan recommendation method of any of the embodiments described in the present disclosure based on instructions stored in the memory.

[0015] According to a fourth aspect of some embodiments of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs a fitness plan recommendation method of any of the embodiments described in the present disclosure.

[0016] According to a fifth aspect of some embodiments of the present disclosure, a computer program product is provided, which, when running on a computer, causes the computer to implement a fitness plan recommendation method of any of the embodiments.

[0017] Other features, aspects, and advantages of the present disclosure will become apparent from the following detailed description of the exemplary embodiments with reference to the following drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The preferred embodiments of the present disclosure will be described herein below with reference to the accompanying drawings. The accompanying drawings are used in the present disclosure to provide a further understanding of the present disclosure and are incorporated and constitute a part of the description of the present disclosure. It should be understood that the accompanying drawings only relate to some embodiments of the present disclosure and do not limit the present disclosure. In the drawings:

[0019] FIG. 1 is a flowchart illustrating a fitness plan recommendation method according to some embodiments of the present disclosure;

[0020] FIG. 2 is a flowchart illustrating a process of generating a plurality of second fitness plans corresponding to the plurality of sub-time periods according to some embodiments of the present disclosure;

[0021] FIG. 3 is a flowchart illustrating a fitness plan recommendation method according to some other embodiments of the present disclosure;

[0022] FIG. 4A is a schematic diagram illustrating a weekly fitness plan according to some embodiments of the present disclosure;

[0023] FIG. 4B is a schematic diagram illustrating a daily fitness plan, according to some embodiments of the present disclosure;

[0024] FIG. 5 is a flowchart illustrating a fitness plan recommendation method, according to some embodiments of the present disclosure;

[0025] FIG. 6 is a block diagram illustrating a fitness plan recommendation device, according to some embodiments of the present disclosure;

[0026] FIG. 7 is a block diagram illustrating a fitness plan recommendation device, according to some embodiments of the present disclosure;

[0027] FIG. 8 illustrates a block diagram of an electronic device, according to some embodiments of the present disclosure.

[0028] It should be understood that the dimensions of the various portions shown in the attached drawings are shown for purposes of convenience and do not necessarily correspond to the actual proportions. Identical or similar reference numerals are used to indicate identical or similar components throughout the attached drawings. Thus, once a component is defined in one drawing, it can not be further discussed in subsequent drawings. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, but not all the embodiments. The description of the embodiments below is actually only illustrative, and should not be construed as any limitation on the present disclosure and its application or use. It should be understood that the present disclosure can be implemented in various forms, and should not be construed as being limited to the embodiments described herein.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present disclosure is not limited in this respect. Unless otherwise specified, the relative arrangement of the components and steps, numerical expressions, and numerical values set forth in these embodiments should be interpreted as merely illustrative, and not limiting the scope of the present disclosure.

[0031] The term "comprise" and variations thereof used in the present disclosure means an open term that includes at least the recited elements / features, but does not exclude other elements / features. In addition, the term "include" and variations thereof used in the present disclosure means an open term that includes at least the recited elements / features, but does not exclude other elements / features. Therefore, include and comprise are synonymous. The term "based on" means "at least partially based on".

[0032] Reference throughout this specification to "one embodiment", "an embodiment", or "embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, however. As used in this specification, the words "can" and "could" mean "should be interpreted as meaning that it is possible. The term "example" is used herein to mean "serving as an example, instance, or illustration." Any aspect of the application described in this detailed description should be considered an example. Any aspect of the application described in this detailed description should be considered an example.

[0033] It should be noted that the terms "first", "second", and so on used in the present disclosure are only used to distinguish different apparatuses, modules, or units, and are not intended to limit the order or interdependence of the functions performed by these apparatuses, modules, or units. Unless otherwise specified, the terms "first", "second", and so on are not intended to imply a given order or any other manner of given order in time, space, ranking, or any other manner.

[0034] It should be noted that the terms "one", "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between the plurality of apparatuses in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0036] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings, but the present disclosure is not limited to these specific embodiments. These specific embodiments below can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments. In addition, in one or more embodiments, specific features, structures, or characteristics can be combined by any suitable means from the present disclosure that will be clear to those skilled in the art.

[0037] A large language model (LLM) is a powerful tool based on artificial intelligence, with the ability to generate and understand natural language. It is a deep learning model trained on large-scale data sets, aiming to simulate human language ability and intelligent thinking process. These models are widely used in natural language processing, dialogue systems, text generation and other language-related tasks.

[0038] The training process of large language models involves massive amounts of text data. It acquires statistical knowledge and semantic understanding of language through self-supervised learning on this data. In the pre-training phase, the model learns to predict the next word or the next sentence in a text to capture contextual and semantic relationships. In this way, the model gradually builds up a deep understanding of language and generation capabilities.

[0039] Large language models can be applied to various specific tasks. It can receive user input and generate corresponding text responses, or generate coherent articles based on given context. The model can perform language translation, document summarization, question answering, and other tasks, and in many cases, it exhibits impressive language understanding and generation capabilities.

[0040] In the related art, the direct one-step generation of a multi-day fitness plan has low accuracy and poor user experience.

[0041] The present disclosure provides a technical solution that can improve the accuracy of fitness plan recommendations and enhance user experience.

[0042] FIG. 1 is a flowchart illustrating a fitness plan recommendation method according to some embodiments of the present disclosure.

[0043] As shown in FIG. 1, the fitness plan recommendation method includes: step S110, in response to receiving a user's request information for obtaining a fitness plan, generating a first fitness plan corresponding to a target time period, wherein the target time period includes a plurality of sub-time periods, and the first fitness plan includes exercise types and exercise parts for the plurality of sub-time periods; step S120, generating a plurality of second fitness plans corresponding to the plurality of sub-time periods according to the first fitness plan, wherein the plurality of second fitness plans include names of a plurality of target fitness actions and fitness guidance content corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan; and step S130, displaying the first fitness plan and the plurality of second fitness plans to the user. The fitness plan recommendation method is executed by, for example, an intelligent agent system. An intelligent agent can also be referred to as a bot.

[0044] In the above embodiment, for the fitness plan of the plurality of sub-time periods, the present disclosure generates exercise types and exercise parts for the plurality of sub-time periods in the first stage, and further refines the first fitness plan based on the exercise types and exercise parts for the plurality of sub-time periods generated in the first stage to generate names of target fitness actions and fitness guidance content for the plurality of sub-time periods. The present disclosure divides the generation process of the fitness plan for the plurality of sub-time periods into two stages, first generating a rough fitness plan, and then generating a detailed fitness plan based on the rough fitness plan. This simplifies complex tasks and can improve the accuracy of fitness plan recommendations.

[0045] In step S110, in response to receiving the request information of the user for obtaining a fitness plan, a first fitness plan corresponding to a target time period is generated, wherein the target time period includes a plurality of sub-time periods, and the first fitness plan includes exercise types and exercise parts of the plurality of sub-time periods.

[0046] In some embodiments, a model can be used to generate the first fitness plan corresponding to the target time period. For example, a generative model can be used to generate the first fitness plan corresponding to the target time period. For another example, a large language model can also be used to generate the first fitness plan corresponding to the target time period.

[0047] In some embodiments, the response to receiving the request information of the user for obtaining a fitness plan includes: performing intent understanding on the request information to obtain fitness intent information of the user; and generating the first fitness plan according to the fitness intent information. For example, a natural language processing model is used to perform intent understanding on the request information.

[0048] In step S120, a plurality of second fitness plans corresponding to the plurality of sub-time periods are generated according to the first fitness plan, wherein the plurality of second fitness plans include names of a plurality of target fitness actions and fitness guidance contents corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan. The name of the target fitness action is, for example, a barbell squat, and the fitness guidance content is, for example, description information guiding the user to complete the barbell squat, which can be textual or visual.

[0049] In some embodiments, the plurality of second fitness plans corresponding to the plurality of sub-time periods can be generated in parallel according to the first fitness plan. In this embodiment, in the second phase, the plurality of second fitness plans of the plurality of sub-time periods are generated in parallel, which can improve the efficiency of fitness plan recommendation.

[0050] In some embodiments, a model can be used to generate the plurality of second fitness plans corresponding to the plurality of sub-time periods in parallel. For example, a generative model can be used to generate the plurality of second fitness plans corresponding to the plurality of sub-time periods in parallel. For another example, a large language model can also be used to generate the plurality of second fitness plans corresponding to the plurality of sub-time periods in parallel.

[0051] Generating the plurality of second fitness plans in parallel using a model, for example, is achieved by concurrently invoking the model, i.e., sending multiple requests to the model at the same time, each request being used to request the generation of at least one second fitness plan.

[0052] In some embodiments, the model used for generating the first fitness plan and the plurality of second fitness plans can be the same model or different models. For example, the model used for generating the first fitness plan and the plurality of second fitness plans is the same large language model, and the large language model generates different fitness plans through different model settings.

[0053] In some embodiments, step S120 of FIG. 1 can be implemented by steps S121-S124 as shown in FIG. 2.

[0054] FIG. 2 is a flow diagram illustrating the generation of a plurality of second fitness plans corresponding to a plurality of sub-time periods, according to some embodiments of the present disclosure.

[0055] As shown in FIG. 2, in step S121, a plurality of reference fitness plans corresponding to the plurality of sub-time periods are generated according to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan, wherein the plurality of reference fitness plans include the names of a plurality of target fitness actions of the plurality of sub-time periods.

[0056] In some embodiments, a plurality of reference fitness plans can be generated in parallel using a model (such as a generative model or a large language model). For example, for the plurality of sub-time periods, the model is called in parallel, and the exercise types and exercise parts of the plurality of sub-time periods are processed to obtain the plurality of reference fitness plans.

[0057] In some embodiments, the plurality of reference fitness plans can also include reference fitness guidance content of the plurality of target fitness actions of the plurality of sub-time periods. This depends on the settings of the model.

[0058] In some embodiments, the plurality of reference fitness plans further include execution manners of the plurality of target fitness actions, wherein the execution manners include execution orders of the plurality of target fitness actions in the plurality of sub-time periods, and numbers of executions of each target fitness action, and the plurality of second fitness plans further include the execution manners of the plurality of target fitness actions.

[0059] In step S122, in the case where the exercise type corresponding to each target fitness action satisfies a first condition, demonstration content of the each target fitness action is obtained from a target content platform. For example, the exercise types can be divided into aerobic exercise and anaerobic exercise, and the first condition is, for example, that the aerobic exercise is a type of exercise that belongs to the follow-along type. The demonstration content is, for example, content for a user to follow along with the fitness action. For example, the target content platform includes but is not limited to a target video platform, and the demonstration content includes but is not limited to a demonstration video. The demonstration video is, for example, a follow-along video.

[0060] In some embodiments, a search plugin can be invoked to match a plurality of videos in a target content platform with each target fitness action to obtain the demonstration content of each fitness action. The plurality of contents in the target content platform have content tags, which can be matched with the name or other description information of each target fitness action to obtain the demonstration content of each fitness action.

[0061] In step S123, in the case where the movement type corresponding to each target fitness action satisfies a second condition, the movement diagram and / or dynamic image of each target fitness action are obtained from a fitness action library according to the name of each target fitness action, wherein the fitness action library includes a plurality of pre-configured names of fitness actions and movement diagrams and / or dynamic images corresponding to each name of fitness action. The fitness action library is constructed according to fitness actions in the fitness field, and can be a professional knowledge base in the fitness field. Taking aerobic exercise and anaerobic exercise as examples, the second condition is that anaerobic exercise does not belong to the movement type of follow-up exercise. In some embodiments, step S122 and step S123 can be executed in parallel to further improve efficiency.

[0062] In some embodiments, the name of each fitness action in the fitness action library includes a first type of name and a second type of name, and the probability of use of the first type of name and the second type of name is different among a plurality of users. The obtaining of the movement diagram and / or dynamic image of each target fitness action from the fitness action library according to the name of each target fitness action includes performing a matching operation on the name of each target fitness action and the first type of name and the second type of name of the plurality of fitness actions in the fitness action library to obtain the movement diagram and / or dynamic image of each target fitness action from the fitness action library.

[0063] In this embodiment, by configuring different categories of names of fitness actions in the fitness action library corresponding to different probabilities of use by a plurality of users, the case where the generated name of the target fitness action does not match the name in the fitness action library is considered, and the matching accuracy and efficiency of the fitness action library are improved, thereby improving the accuracy and efficiency of the fitness plan recommendation.

[0064] In some embodiments, the probability of use of the first type of name of each fitness action is higher than the probability of use of the second type of name of each fitness action among a plurality of users. The matching operation on the name of each target fitness action and the first type of name and the second type of name of the plurality of fitness actions in the fitness action library to obtain the movement diagram and / or dynamic image of each target fitness action from the fitness action library includes the following operations.

[0065] First, a first matching operation is performed on the name of each target fitness action and a first type of name of the plurality of fitness actions in the fitness action library.

[0066] Then, in the case where the first matching operation fails, a second matching operation is performed on the name of each target fitness action and a second type of name of the plurality of fitness actions in the fitness action library, to obtain the exercise diagram and / or dynamic image of each target fitness action from the fitness action library.

[0067] Finally, in the case where the first matching operation succeeds, the exercise diagram and / or dynamic image corresponding to the first type of name matching the name of each target fitness action is obtained from the fitness action library as the exercise diagram and / or dynamic image of each target fitness action.

[0068] In this embodiment, the first type of name with a higher probability of being used in a plurality of users is preferentially matched, which can further improve the efficiency of matching, thereby further improving the efficiency of fitness plan recommendation.

[0069] In step S124, the plurality of second fitness plans are generated according to at least one of the demonstration content, exercise diagram and dynamic image of the plurality of target fitness actions and the plurality of reference fitness plans, wherein the fitness guidance content of the plurality of target fitness actions in the plurality of second fitness plans includes at least one of the demonstration content, exercise diagram and dynamic image of the plurality of target fitness actions.

[0070] In the above embodiment, based on steps S121-S124, by judging whether the exercise type satisfies the first condition or the second condition, combining content search of the content platform and search of the exercise diagram and / or dynamic image of the fitness action library, the fitness plan is more suitable for the actual fitness scene, and a more accurate fitness plan is recommended for the user, thereby further improving the accuracy of fitness plan recommendation and improving the user experience.

[0071] In some embodiments, the fitness action library further includes at least one of a fitness action guide and a fitness action skill corresponding to the names of the plurality of fitness actions, and the generating the plurality of second fitness plans according to at least one of the demonstration content, exercise diagram and dynamic image of the plurality of target fitness actions and the plurality of reference fitness plans includes:

[0072] In the case where the exercise type corresponding to each target fitness action satisfies the second condition, at least one of the fitness action guide and the fitness action skill of each target fitness action is obtained from the fitness action library according to the name of each target fitness action;

[0073] generate the plurality of second fitness plans according to at least one of the demonstration content, the motion diagram and the dynamic image of the plurality of target fitness actions, the plurality of reference fitness plans, and at least one of the fitness action guide and the fitness action skill of each target fitness action, wherein the fitness guidance content of the plurality of target fitness actions in the plurality of second fitness plans further comprises at least one of the fitness action guide and the fitness action skill of the plurality of target fitness actions.

[0074] In this embodiment, the fitness action guide and the fitness action skill of the target fitness action are provided through the pre-configured fitness action library, so that the recommended fitness plans for different users or the same user at different times are relatively stable, and the stability of the generated fitness plans is improved. The fitness action guide is a text description of how to guide the user to perform the fitness action. The fitness action skill is a text description of the action skill of the user in the process of performing the fitness action.

[0075] Returning to FIG. 1, in step S130, the first fitness plan and the plurality of second fitness plans are displayed to the user.

[0076] In some embodiments, displaying the first fitness plan and the plurality of second fitness plans to the user comprises: rendering the first fitness plan in a first card; rendering each second fitness plan in a second card corresponding to the second fitness plan; displaying the first card to the user and hiding the plurality of second cards corresponding to the plurality of second fitness plans; and in response to the user performing a trigger operation on the first card, displaying the second card corresponding to the trigger operation to the user. In this embodiment, the first card is jumped to the plurality of second cards by constructing the association between the first card and the second card. The first fitness plan and the plurality of second fitness plans are displayed or exhibited to the user in the card mode, which can further improve the user experience.

[0077] In some embodiments, the first fitness plan comprises a plurality of first sub-fitness plans corresponding one-to-one to the plurality of sub-time periods, the plurality of first sub-fitness plans correspond one-to-one to the plurality of second fitness plans, and displaying the first fitness plan and the plurality of second fitness plans to the user comprises: rendering the plurality of first sub-fitness plans in a first card; rendering the second fitness plan corresponding to each first sub-fitness plan in a second card corresponding to the first sub-fitness plan; displaying the first card to the user and hiding the second card; and in response to the user performing a trigger operation on any first sub-fitness plan in the first card, displaying the second card corresponding to the any first sub-fitness plan to the user.

[0078] In some embodiments, before the first fitness plan and the plurality of second fitness plans are presented to the user, a fitness summary information is generated according to the first fitness plan and the plurality of second fitness plans and sent to the user. For example, the fitness summary message includes information such as fitness goals for a target time period, expected fitness effects to be achieved, and fitness considerations.

[0079] In the following, the fitness plan recommendation method in some embodiments of the present disclosure will be described in detail taking the generation of a weekly fitness plan as an example.

[0080] FIG. 3 is a flowchart illustrating a fitness plan recommendation method according to some other embodiments of the present disclosure.

[0081] As shown in FIG. 3, the user sends request information for obtaining a fitness plan to the intelligent agent, and the intelligent agent returns a prompt message of “plan generating, please wait later” to the user. The intelligent agent calls a large model (a large language model) to generate a simple plan. The simple plan includes the exercise type and exercise part for each of the seven days in a week. For example, the simple plan includes the exercise type and exercise part for each of Monday to Friday, and the exercise type and exercise part are null values for Saturday and Sunday. The prompt message and the process of calling the large model to generate the simple plan are executed asynchronously by the intelligent agent and returned to the user.

[0082] The intelligent agent can use computer program modules (codes) to date supplement the simple plan. For example, the user requests a fitness plan from March 3, 20YY to March 9, 20YY, and the intelligent agent generates a fitness plan for the seven days in a week, and then uses a date supplement function or program to supplement the date of March 3, 20YY to March 9, 20YY for each day of the simple plan.

[0083] The intelligent agent calls the large model to generate multi-day detailed daily plans according to the simple plan. For example, the intelligent agent can call the large model to generate multi-day detailed daily plans in parallel according to the simple plan, and the degree of parallelism of the large model is determined according to the number of days with non-null values of the exercise type and exercise part. For example, the exercise type and exercise part are non-null values from Monday to Friday, and the degree of parallelism of the large model is 5. Referring to FIG. 3, the detailed daily plans for Monday to Friday are generated by calling the large model in parallel. For example, the five days from Monday to Friday can be divided into aerobic exercise days and anaerobic exercise days. The large model can have two settings, one for aerobic setting and one for anaerobic setting. The detailed daily plans for Monday to Friday include anaerobic daily plans and aerobic daily plans.

[0084] As shown in FIG. 3, the intelligent agent can also use the large model to perform natural language processing on the simple plan and the multi-day detailed daily plans to obtain fitness summary information and send the fitness summary information to the user.

[0085] Referring to FIG. 3, the agent supplements the exercise move of the detailed daily plan with the exercise illustration and / or dynamic image of the exercise move by querying the exercise move library, in the case that the exercise move is an anaerobic exercise move. For example, in the case that the exercise move library does not include the exercise illustration and / or dynamic image of the exercise move of the detailed daily plan, the agent searches from the target content platform to obtain at least one of the exercise illustration, dynamic image, and demonstration content of the exercise move of the detailed daily plan as a supplement to the detailed daily plan.

[0086] Table 1 shows a data structure of the exercise move library. As shown in Table 1, for example, the exercise move library includes the name of the exercise move, the exercise illustration or dynamic image, the move description, the body part for exercise, the exercise equipment, the primary muscle group for exercise, the secondary muscle group, the exercise move tutorial, and the exercise move tips, etc. The exercise move library can be stored in a database. Table 1 only shows the information of one exercise move, and in fact, the exercise move library includes the information of multiple exercise moves.

[0087] For example, the field of the name is denoted as “fitness_move_name”, the field of the exercise illustration or dynamic image is denoted as “fitness_move_gif”, the field of the move description is denoted as “fitness_move_description”, the field of the body part for exercise is denoted as “body_part”, the field of the exercise equipment is denoted as “equipment”, the field of the primary muscle group for exercise is denoted as “primary_muscles”, the field of the secondary muscle group is denoted as “secondary_muscles”, the field of the exercise move tutorial is denoted as “fitness_move_tutorial”, and the field of the exercise move tips is denoted as “fitness_move_tips”.

[0088] For example, referring to Table 1, the exercise move library stores the dynamic image 1 corresponding to the exercise move “barbell front squat”, the move description “description content 1”, the exercise part “hips”, the exercise equipment “barbell”, the primary muscle group “gluteus maximus, quadriceps”, the secondary muscle group “adductor magnus, soleus”, the exercise move tutorial “tutorial 1”, and the exercise move tips “tips 1”.

[0089] For example, the description content 1 corresponding to the fitness action "barbell front squat" includes "The barbell front squat is a strength training exercise that primarily targets the quadriceps, glutes, and core muscles, while also working the upper body and improving overall balance. This exercise is ideal for athletes, weightlifters, and fitness enthusiasts seeking to enhance lower body strength, increase muscle mass, and improve functional health. People can choose to incorporate this exercise into their daily routine to achieve better body composition, improve athletic performance, and promote more effective movement patterns in daily life."

[0090] For example, the guideline 1 corresponding to the fitness action "barbell front squat" includes "Carefully lift the barbell from the rack, then step back one step with your feet shoulder-width apart, toes slightly outward. Bend your knees and hips, lower your body, keeping your back straight, chest up, until your thighs are parallel to the floor. Forcefully stand back to the starting position with your heels, keeping your core tight and the barbell in place on your shoulders. Repeat the exercise for the desired number of repetitions, then carefully put the barbell back on the rack.

[0091] For example, the "technique 1" corresponding to the fitness action "barbell front squat" includes "Correct foot placement: Feet should be shoulder-width apart or slightly wider. Slightly outwardly point your toes. Incorrect foot placement can lead to instability and potential injury. Maintain spinal alignment: Another common mistake is to arch your back during the squat. To avoid this, focus on keeping your chest up and maintaining spinal alignment throughout the movement. This helps protect your back and ensures that you are exercising the correct muscles. Proper depth: The goal is to lower your body until your thighs are at least parallel to the floor."

[0092] Table 1 Data structure of fitness action library

[0093] Referring to FIG. 3, the agent supplements the detailed daily plan for the fitness action with the type of exercise being aerobic exercise by searching from the target content platform to obtain the demonstration content of the fitness action in the detailed daily plan.

[0094] As shown in FIG. 3, after the agent completes the supplementation of the multi-day detailed daily plan, the simple plan and the multi-day detailed daily plan are saved, and a card rendered with the simple plan and the multi-day detailed daily plan is generated and sent to the user. The form of the card can refer to FIG. 4A and FIG. 4B.

[0095] In FIG. 4A, a card of a simple plan for a week is shown to the user. FIG. 4A is a schematic diagram illustrating a weekly fitness plan according to some embodiments of the present disclosure.

[0096] As shown in FIG. 4A, the exercise type of Monday is exercise type 1, the exercise part is exercise part 1, the exercise type of Tuesday is exercise type 2, the exercise part is exercise part 2, the exercise type of Wednesday is exercise type 3, the exercise part is exercise part 3, the exercise type of Thursday is exercise type 4, the exercise part is exercise part 4, the exercise type of Friday is exercise type 5, the exercise part is exercise part 5. The exercise type and the exercise part of Saturday and Sunday are null, showing rest.

[0097] The user clicks the item of Tuesday in the card showing the simple plan of the week, and the card jumps to the card showing the detailed daily plan of Tuesday shown in FIG. 4B.

[0098] FIG. 4B is a schematic diagram showing a daily fitness plan according to some embodiments of the present disclosure.

[0099] As shown in FIG. 4B, taking the detailed daily plan of Tuesday as an example of leg anaerobic exercise, the plurality of target fitness actions include deep squat, Romanian deadlift, leg curl. In the card showing the detailed daily plan of Tuesday, each target fitness action corresponds to a dynamic image for guiding the user to correctly perform each target fitness action. Deep squat corresponds to dynamic image 1, Romanian deadlift corresponds to dynamic image 2, and leg curl corresponds to dynamic image 3.

[0100] In some embodiments, in the card showing the detailed daily plan of Tuesday shown in FIG. 4B, the number of sets of performing each target fitness action, the number of each set, and the sub-part of the exercise part corresponding to each target fitness action can also be displayed. For example, deep squat is 4 sets, each set of 12; Romanian deadlift is 4 sets, each set of 10; leg curl is 3 sets, each set of 15. For another example, deep squat exercises the hip, Romanian deadlift exercises the hip, and leg curl exercises the hamstring and the thigh.

[0101] In some embodiments, in the card showing the detailed daily plan of Tuesday shown in FIG. 4B, the rest time between different target fitness actions can also be displayed (not shown in FIG. 4B).

[0102] In some embodiments, as shown in FIG. 4A and FIG. 4B, the total fitness time and the total energy consumption of each day can also be displayed in the card (not shown in FIG. 4A and FIG. 4B).

[0103] FIGS. 3-4B are only an example of the present disclosure, and do not specifically limit the present disclosure. For example, the division of anaerobic and aerobic can be configured according to actual conditions.

[0104] FIG. 5 is a flowchart showing a fitness plan recommendation method according to some embodiments of the present disclosure.

[0105] As shown in FIG. 5, taking the user's request to generate a fitness plan for a week as an example, the fitness plan recommendation method includes steps S500-S570.

[0106] In step S500, the user requests the bot engine to generate a fitness plan.

[0107] In step S510, the bot engine performs intent recognition on the user's request, and controls a workflow to start an operation of generating a fitness plan, which includes a simple plan for a week (a weekly fitness plan) and a daily detailed plan for each day in a week (a daily fitness plan).

[0108] In step S520, the workflow starts an operation of searching for an exercise diagram and / or a dynamic image corresponding to anaerobic exercise in an exercise action library, and supplementing the exercise diagram and / or the dynamic image corresponding to the anaerobic exercise into the daily detailed plan corresponding to the anaerobic exercise. The exercise action library is, for example, a database structure.

[0109] In step S530, the workflow starts an operation of searching for demonstration content corresponding to aerobic exercise on a target content platform, and supplementing the demonstration content corresponding to the aerobic exercise into the daily detailed plan corresponding to the aerobic exercise.

[0110] In step S540, the workflow starts an operation of sending the fitness plan to a storage module.

[0111] In step S550, the storage module stores the weekly fitness plan and the daily fitness plans for a week.

[0112] In step S560, the workflow starts an operation of generating a card by a local plug-in according to the weekly fitness plan and the daily fitness plans for a week stored in the storage module.

[0113] In step S570, the local plug-in returns the generated card to the user, and displays the weekly fitness plan and the daily fitness plans for a week to the user in the form of the card.

[0114] The bot engine, the workflow, the exercise action library, the storage module, and the local plug-in in FIG. 5 jointly constitute a bot system. For example, the bot system becomes a healthy life bot.

[0115] FIG. 5 is only an example of the present disclosure, and does not specifically limit the fitness plan recommendation method of the present disclosure.

[0116] The above is the fitness plan recommendation method provided by some embodiments of the present disclosure. In the following, a fitness plan recommendation device in some embodiments of the present disclosure will be described in conjunction with FIG. 6.

[0117] FIG. 6 is a block diagram illustrating a fitness plan recommendation device according to some embodiments of the present disclosure.

[0118] As shown in FIG. 6, the fitness plan recommendation apparatus 6 comprises a first generating module 61, a second generating module 62, and a presenting module 63.

[0119] The first generating module 61 is configured to, in response to receiving the request information of the user for obtaining a fitness plan, generate a first fitness plan corresponding to a target time period, wherein the target time period comprises a plurality of sub-time periods, and the first fitness plan comprises exercise types and exercise parts of the plurality of sub-time periods.

[0120] The second generating module 62 is configured to, according to the first fitness plan, generate a plurality of second fitness plans corresponding to the plurality of sub-time periods, wherein the plurality of second fitness plans comprise names of a plurality of target fitness actions and fitness guidance contents corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan.

[0121] The presenting module 63 is configured to present the first fitness plan and the plurality of second fitness plans to the user.

[0122] The fitness plan recommendation apparatus 6 can be used to perform steps S110-S130 of FIG. 1. In some embodiments, the fitness plan recommendation apparatus 6 can also perform any steps in other embodiments of the present disclosure.

[0123] It should be noted that each of the above modules is only a logical module according to the specific function implemented by it, and is not used to limit the specific implementation manner, for example, it can be implemented in software, hardware, or a combination of software and hardware. In actual implementation, each of the above modules can be implemented as an independent physical entity, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). In addition, each of the above modules is indicated by a dashed line in the drawing, indicating that these modules can not actually exist, and the operations / functions implemented by them can be implemented by the processing circuit itself.

[0124] The above is the fitness plan recommendation apparatus in some embodiments of the present disclosure.

[0125] FIG. 7 is a block diagram illustrating a fitness plan recommendation apparatus according to some embodiments of the present disclosure.

[0126] As shown in FIG. 7, the fitness plan recommendation apparatus 7 comprises a memory 71 and a processor 72 coupled to the memory 71, wherein the processor 72 is configured to perform the fitness plan recommendation method according to any of the preceding embodiments based on instructions stored in the memory 71.

[0127] The memory 71 is configured to store one or more computer-readable instructions. The memory 71 can include any combination of various types of computer-readable storage media, such as volatile memory and / or non-volatile memory including, but not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), flash memory. The memory 71 may, for example, store an operating system, application programs, a boot loader, a database, and other programs, and can also store various application programs and various data.

[0128] The processor 72 is configured to run the computer-readable instructions to implement the fitness plan recommendation method of any of the preceding embodiments. For specific implementation of each step of the fitness plan recommendation method, please refer to the above-described embodiments, and the repeated parts will not be described here.

[0129] The processor 72 and the memory 71 can communicate with each other directly or indirectly. For example, the processor 72 and the memory 71 can communicate through a network. The network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network. The processor 72 and the memory 71 can also communicate with each other through a system bus, and the present disclosure does not limit the implementation.

[0130] It should be noted that the components of the fitness plan recommendation device 7 shown in FIG. 7 are exemplary and not limiting, and the fitness plan recommendation device 7 can also have other components according to actual application needs. The processor 72 can perform the desired functions with other components of the fitness plan recommendation device 7.

[0131] The fitness plan recommendation device can be implemented by software, firmware, and / or hardware, and can be integrated into an electronic device installed with a related application program.

[0132] FIG. 8 shows a block diagram of an electronic device according to some embodiments of the present disclosure.

[0133] The electronic device 8 shown in FIG. 8 can be a computer system with a dedicated hardware structure, which can perform corresponding functions when a related application program is installed.

[0134] The electronic device includes, but is not limited to, a mobile terminal such as a smartphone, a notebook computer, a personal digital assistant (PDA), a tablet computer (Tablet PC), a PMP (portable multimedia player), a vehicle terminal (such as a vehicle navigation terminal), a wearable device, and the like, and a fixed terminal such as a digital television, a desktop computer, and the like.

[0135] As shown in FIG. 8, a central processing unit (CPU) 81 executes various processes in accordance with a program stored in a read only memory (ROM) 82 or a program loaded from a storage section 88 to a random access memory (RAM) 83. In the RAM 83, data required when the CPU 81 executes various processes and the like is stored as necessary. The central processing unit is merely exemplary, and can be other types of processors, such as the various processors described above. The ROM 82, the RAM 83, and the storage section 88 can be various forms of computer readable storage media. Note that although the ROM 82, the RAM 83, and the storage section 88 are shown separately in FIG. 8, one or more of them can be combined, or located in the same or different memory or storage modules.

[0136] The CPU 81, the ROM 82, and the RAM 83 are connected to each other via a bus 84. An input / output interface 85 is also connected to the bus 84.

[0137] The following components are connected to the input / output interface 85: an input section 86 including a touch panel, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, and the like; an output section 87 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage section 88 including a hard disk, a magnetic tape, and the like; and a communication section 89 including a network interface card such as a LAN card, a modem, and the like. The communication section 89 allows communication processing to be performed via a network such as the Internet. It is easily understood that although the various devices or modules in the electronic device 8 are shown in FIG. 8 as communicating via the bus 84, they can also communicate through a network or other means, where the network can include a wireless network, a wired network, and / or any combination of a wireless network and a wired network.

[0138] A drive 810 is also connected to the input / output interface 85 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive 810 as necessary, so that a computer program read therefrom is installed in the storage section 88 as necessary.

[0139] In the case where the above series of processes are implemented by software, the program constituting the software can be installed from a network such as the Internet or a storage medium 811 or the like.

[0140] According to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product that, when run on a computer, causes the computer to implement the fitness plan recommendation method described in any of the preceding embodiments. The computer program product includes a computer program carried on a computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from the network by the communication section 89, or installed from the storage section 88, or installed from the ROM 82. When the computer program is executed by the CPU 81, the fitness plan recommendation method of the embodiments of the present disclosure is executed.

[0141] Note that, in the context of the present disclosure, the computer-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0142] The computer-readable medium can be a computer-readable storage medium, or a computer-readable signal medium, or any combination of the two.

[0143] The computer-readable storage medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the fitness plan recommendation method described in any of the preceding embodiments.

[0144] The computer readable medium can include a computer-readable signal medium and / or computer-readable storage medium. A computer readable signal medium can include a propagated data signal with computer executable instructions. A computer readable storage medium can include any non-transitory medium that can store computer executable instructions such as volatile memory or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, or a combination of the two. The computer readable storage medium can include, but is not limited to, volatile memory (e.g., DRAM, SRAM, etc.), non-volatile memory (e.g., ROM, PROM, and EPROM, EEPROM, Flash memory, etc.), and memory devices. The computer readable storage medium can also include, but is not limited to, magnetic storage devices (e.g., disk drives, magnetic tape, etc.), optical storage devices (e.g., CD-ROM, DVD, etc.), solid state devices (e.g., SSD, flash memory, etc.), or any device that is suitable for storing computer executable instructions.

[0145] The computer readable medium can be included within the electronic device; or can exist separately from the electronic device.

[0146] In some embodiments, a computer program product is also provided, which, when running on a computer, causes the computer to implement the fitness plan recommendation method according to any of the above embodiments.

[0147] In some embodiments, a computer program is also provided, which comprises instructions, which, when executed on a processor, cause the processor to perform the fitness plan recommendation method according to any of the above embodiments. For example, the instructions can be embodied in computer program code.

[0148] In an embodiment of the disclosure, the computer program code for carrying out operations of the disclosure can be written in one or more programming languages or combinations of languages including object oriented programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0149] The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed by one or more processors of a computing device, cause the one or more processors to perform the operations of the method of the first aspect. The one or more non-transitory computer-readable media can include one or more of the following: a magnetic storage device, an optical storage device, a solid-state storage device, a hard disk drive, a flash drive, a RAM, a ROM, a database, and / or any other suitable type of non-transitory computer-readable medium.

[0150] The functions described above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, non-limiting examples of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0151] While certain aspects of the present disclosure have been described with reference to particular examples, those of ordinary skill in the art will understand that various other modifications can be made to the examples and in other aspects, without departing from the scope and spirit of the disclosure. It is not intended that the disclosure be limited as described above, but instead that the scope of the disclosure be measured by the broadest interpretation of the following claims.

Claims

1. A fitness plan recommendation method, comprising: generating a first fitness plan corresponding to a target time period in response to receiving a request information of a user for obtaining a fitness plan, wherein the target time period comprises a plurality of sub-time periods, and the first fitness plan comprises exercise types and exercise parts of the plurality of sub-time periods; generating a plurality of second fitness plans corresponding to the plurality of sub-time periods according to the first fitness plan, wherein the plurality of second fitness plans comprise names and fitness guidance contents of a plurality of target fitness actions corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan; showing the first fitness plan and the plurality of second fitness plans to the user.

2. The exercise program recommendation method according to claim 1, wherein, The generating a plurality of second fitness plans corresponding to the plurality of sub-time periods according to the first fitness plan comprises: generating a plurality of reference fitness plans corresponding to the plurality of sub-time periods according to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan, wherein the plurality of reference fitness plans comprise names of a plurality of target fitness actions of the plurality of sub-time periods; in a case where an exercise type corresponding to each target fitness action satisfies a first condition, obtaining demonstration content of the each target fitness action from a target content platform; in a case where the exercise type corresponding to the each target fitness action satisfies a second condition, obtaining an exercise diagram and / or a dynamic image of the each target fitness action from a fitness action library according to the name of the each target fitness action, wherein the fitness action library comprises pre-configured names of a plurality of fitness actions and exercise diagrams and / or dynamic images corresponding to the names of the plurality of fitness actions; generating the plurality of second fitness plans according to at least one of demonstration content, exercise diagrams and dynamic images of the plurality of target fitness actions and the plurality of reference fitness plans, wherein fitness guidance contents of the plurality of target fitness actions in the plurality of second fitness plans comprise at least one of the demonstration content, exercise diagrams and dynamic images of the plurality of target fitness actions.

3. The exercise program recommendation method according to claim 2, wherein, The names of each fitness action in the fitness action library comprise first type names and second type names, probabilities of use of the first type names and the second type names in a plurality of users are different, and the obtaining an exercise diagram and / or a dynamic image of the each target fitness action from a fitness action library according to the name of the each target fitness action comprises: performing a matching operation on the name of the each target fitness action and the first type names and the second type names of the plurality of fitness actions in the fitness action library to obtain the exercise diagram and / or the dynamic image of the each target fitness action from the fitness action library.

4. The exercise program recommendation method according to claim 3, wherein, The probability of the first type of name of each fitness action being used in the plurality of users is higher than the probability of the second type of name of each fitness action being used in the plurality of users, the name of each target fitness action is matched with the first type of name and the second type of name of the plurality of fitness actions in the fitness action library to perform a matching operation, and the motion diagram and / or dynamic image of each target fitness action is obtained from the fitness action library, which comprises: performing a first matching operation on the name of each target fitness action and the first type of name of the plurality of fitness actions in the fitness action library; in the case where the first matching operation fails, performing a second matching operation on the name of each target fitness action and the second type of name of the plurality of fitness actions in the fitness action library to obtain the motion diagram and / or dynamic image of each target fitness action from the fitness action library; in the case where the first matching operation succeeds, obtaining the motion diagram and / or dynamic image corresponding to the first type of name matched with the name of each target fitness action from the fitness action library as the motion diagram and / or dynamic image of each target fitness action.

5. The fitness plan recommendation method according to any one of claims 2-4, wherein, The fitness action library further comprises at least one of a fitness action guide and a fitness action skill corresponding to the name of the plurality of fitness actions, and the generating of the plurality of second fitness plans according to at least one of the demonstration content, the motion diagram and the dynamic image of the plurality of target fitness actions and the plurality of reference fitness plans comprises: in the case where the motion type corresponding to each target fitness action satisfies a second condition, obtaining at least one of the fitness action guide and the fitness action skill of each target fitness action from the fitness action library according to the name of each target fitness action; generating the plurality of second fitness plans according to at least one of the demonstration content, the motion diagram and the dynamic image of the plurality of target fitness actions, the plurality of reference fitness plans and at least one of the fitness action guide and the fitness action skill of each target fitness action, wherein the fitness guidance content of the plurality of target fitness actions in the plurality of second fitness plans further comprises at least one of the fitness action guide and the fitness action skill of the plurality of target fitness actions. The generating of the plurality of reference fitness plans according to the motion type and the exercise part of the plurality of sub-time periods in the first fitness plan comprises:

6. The fitness program recommendation method according to any one of claims 2-4, wherein, for the plurality of sub-time periods, calling a model in parallel to process the motion type and the exercise part of the plurality of sub-time periods to obtain the plurality of reference fitness plans. The plurality of reference fitness plans further comprise the execution mode of the plurality of target fitness actions, wherein the execution mode comprises the execution order of the plurality of target fitness actions in the plurality of sub-time periods and the number of executions of each target fitness action, and the plurality of second fitness plans further comprise the execution mode of the plurality of target fitness actions.

7. The fitness program recommendation method according to any one of claims 2 to 4, wherein, The displaying of the first fitness plan and the plurality of second fitness plans to the user comprises:

8. The exercise program recommendation method according to any one of claims 1 to 4, wherein, rendering the first fitness plan in a first card; ​ rendering each second fitness plan in a second card corresponding to each second fitness plan; showing the first card to the user and hiding the second cards corresponding to the second fitness plans; in response to the user performing a trigger operation on the first card, showing the second card corresponding to the trigger operation to the user. 9.The fitness plan recommendation method of any one of claims 1-4, further comprising: before showing the first fitness plan and the second fitness plans to the user, generating and sending fitness summary information to the user according to the first fitness plan and the second fitness plans.

10. The exercise program recommendation method according to any one of claims 1-4, wherein, The generating the first fitness plan corresponding to the target time period in response to receiving the request information for obtaining the fitness plan from the user comprises: performing intent understanding on the request information to obtain fitness intent information of the user; generating the first fitness plan according to the fitness intent information. 11.A fitness plan recommendation apparatus, comprising: a first generating module configured to generate a first fitness plan corresponding to a target time period in response to receiving a request information for obtaining the fitness plan from a user, wherein the target time period comprises a plurality of sub-time periods, and the first fitness plan comprises exercise types and exercise parts of the plurality of sub-time periods; a second generating module configured to generate a plurality of second fitness plans corresponding to the plurality of sub-time periods according to the first fitness plan, wherein the plurality of second fitness plans comprise names and fitness guidance contents of a plurality of target fitness actions corresponding to the exercise types and exercise parts of the plurality of sub-time periods in the first fitness plan; a showing module configured to show the first fitness plan and the second fitness plans to the user. 12.A fitness plan recommendation apparatus, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the fitness plan recommendation method of any one of claims 1-10 based on instructions stored in the memory. 13.A computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the fitness plan recommendation method of any one of claims 1-10. 14.A computer program product, which, when executed on a computer, causes the computer to implement the fitness plan recommendation method of any one of claims 1-10.

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