CN114937485A - Method, device and system for realizing fitness guidance - Google Patents
Method, device and system for realizing fitness guidance Download PDFInfo
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Abstract
The application discloses a method for realizing body-building guidance, which comprises the following steps: on a client side, obtaining fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise exercise force characteristic attributes used for representing exercise capacity, determining a matched fitness plan for the target user based on the fitness characteristic attributes, and in the process of implementing the fitness plan, obtaining first real-time data associated with fitness of the target user, wherein the first real-time data at least comprises real-time data used for exercise force detection, performing fitness detection by using the first real-time data, wherein the fitness detection at least comprises exercise force detection data, adjusting the fitness characteristic attributes at least according to the exercise force detection data, and determining a matched next fitness plan for the target user based on the adjusted fitness characteristic attributes. The intelligent fitness guidance system improves the intelligence of fitness guidance, enables fitness to be more scientific and efficient, and achieves the guidance effect of thousands of people.
Description
Technical Field
The invention relates to the field of sports and fitness, in particular to a method for realizing fitness guidance.
Background
With the progress and development of the technology, the application of sports fitness in the aspects of fitness equipment, electronic clients and the like is enriched. From the guidance of exercise and fitness, the following methods mainly exist at present:
the method comprises the following steps of firstly, guiding through fitness and personal education under a line;
the method II is guided by the interaction of an online audio and video technology;
and thirdly, guiding the motion action through an intelligent vision technology. For example, motion actions are guided by visual detection techniques.
Most of the above-mentioned exercise and fitness guidance is for a specific exercise item, and there is no systematic guidance from the whole user.
Disclosure of Invention
The invention provides a method for realizing fitness guidance, which provides intelligent and systematic fitness guidance for a user.
The invention provides a method for realizing body-building guidance, which comprises the following steps: on the side of the client side,
acquiring fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise movement force characteristic attributes used for representing movement capacity,
determining a matching fitness plan for the target user based on the fitness characteristic attributes,
acquiring first real-time data associated with the target user and the fitness during the exercise plan, wherein the first real-time data at least comprises real-time data for detecting the movement force,
performing fitness detection by using the first real-time data, wherein the fitness detection at least comprises exercise force detection data,
and adjusting the fitness characteristic attribute at least according to the movement force detection data, and determining a next fitness plan matched with the target user based on the adjusted fitness characteristic attribute.
In a second aspect, the present invention provides a device for performing fitness training, the device comprising:
a fitness characteristic attribute acquisition module for acquiring fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise a movement force characteristic attribute for representing movement capacity,
a fitness plan matching module for determining a matched fitness plan for the target user based on the fitness characteristic attributes,
a process data acquisition module for acquiring first real-time data associated with the fitness of the target user during the implementation of the fitness plan, wherein the first real-time data at least comprises real-time data for detecting the movement force,
the body-building detection module is used for carrying out body-building detection by utilizing the first real-time data, wherein the body-building detection at least comprises movement force detection data,
and the adjusting module is used for adjusting the fitness characteristic attribute at least according to the movement force detection data and determining a next matched fitness plan for the target user based on the adjusted fitness characteristic attribute.
The invention provides a system for realizing body-building guidance, which comprises a collecting device and electronic equipment, wherein the collecting device is used for collecting real-time data in the movement process, the electronic equipment is provided with a client, and a communication link is established between the collecting device and the electronic equipment;
the electronic device comprising a memory storing a computer program and a processor configured to execute the steps of the computer program to implement any of the exercise guidance implementations,
preferably, the system further comprises an exercise machine, wherein a communication link is established between the exercise machine with the sensor and the electronic device;
the acquisition device comprises an image acquisition device for acquiring moving image data in the movement process and/or a sensing device for acquiring body indexes in the movement process.
A fourth aspect of the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the fitness guide implementing methods.
According to the method for realizing fitness guidance, the fitness characteristic attribute of the target user is determined by combining the exercise force detection data, so that the fitness plan matched based on the fitness characteristic attribute can be started from the whole target user, the systematicness of the fitness guidance is improved, the real-time data of the fitness plan execution process is used for evaluation, and the fitness characteristic attribute is adjusted based on the evaluation result data, so that the fitness plan is adjusted, the intelligence of the fitness guidance is improved, and the fitness is more scientific and efficient. From the perspective of the user, the guiding effect of thousands of people and thousands of faces is achieved.
Drawings
FIG. 1 is a schematic flow chart of a method for implementing exercise guidance according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a method for implementing the exercise guidance according to the embodiment of the present application.
Fig. 3 is a schematic flow chart illustrating a process of acquiring first data associated with fitness of a target user at an initial stage.
FIG. 4 is a schematic representation of the fitness feature attributes of a target user.
FIG. 5 is a schematic diagram of generating a matched fitness plan for a target user based on fitness characteristics attributes.
Fig. 6 is a schematic diagram of a first motion process image data for real-time motion normative detection and guidance.
Fig. 7 is a schematic diagram of detecting motion force by using the first real-time data.
Fig. 8 is a schematic view of an apparatus for performing exercise guidance.
FIG. 9 is a schematic diagram of a system for implementing exercise guidance.
Detailed Description
For the purpose of making the objects, technical means and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for implementing the exercise guidance according to the embodiment of the present application. The embodiment is applied to a client, for example, a client on the intelligent terminal side. The method comprises the following steps:
step 104, utilizing the first real-time data to perform fitness detection, wherein the fitness detection at least comprises exercise force detection data,
and 105, adjusting the fitness characteristic attribute at least according to the movement force detection data, and determining a next fitness plan matched with the target user based on the adjusted fitness characteristic attribute.
The method comprises the steps of constructing a motion portrait for a target user by acquiring fitness characteristic attributes of the target user; the sports force characteristic attribute is taken as one of fitness characteristic attributes, so that the accuracy of the sports portrait is improved; the fitness plan matched based on the fitness characteristic attributes improves the adaptability and systematicness of fitness guidance; the fitness plan is adjusted by utilizing the first real-time data acquired in the fitness plan implementing process and adjusting the fitness characteristic attributes based on the fitness detection, so that closed-loop feedback is formed from the fitness plan matching to the fitness plan implementing process, the closed-loop feedback is also formed on the fitness characteristic attributes, the intelligence and the real-time performance of the fitness plan are improved, the intelligence of fitness guidance is improved, and the effects of scientific fitness and healthy movement are achieved.
The following detailed description is provided to facilitate understanding of the present application.
Referring to fig. 2, fig. 2 is a schematic diagram of a method for implementing the exercise guidance according to the embodiment of the present application. In the figure, the exercise picture corresponds to the fitness characteristic attribute, the exercise prescription corresponds to the fitness plan, the exercise real-time detection and guidance correspond to the acquisition of the first real-time data and the implementation of the fitness plan, and the exercise evaluation corresponds to the fitness detection. In particular, the method of manufacturing a semiconductor device,
step 201, obtaining fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise a movement force characteristic attribute for representing movement capacity,
as an example, referring to fig. 3, fig. 3 is a schematic flow chart illustrating a process of obtaining first data associated with fitness of a target user at an initial stage.
In an initial stage, first data associated with fitness of a target user may be acquired, wherein the first data may include static data for characterizing static characteristic attributes of the target user and dynamic data for characterizing motor abilities of the target user,
static data may be obtained through targeted user feedback and/or data collection by sensors, including and not limited to:
basic information such as height, weight, age, sex, medical history, occupation, physical index in a static state, and the like;
fitness purposes, e.g., fat reduction, muscle enhancement, shaping, physical enhancement, etc.;
favoring sports items, such as, for example, skipping ropes, cycling, running, rowing, exercising, etc.;
exercise habits including self-assessment data and self-selected exercise times, e.g., exercise type, exercise frequency, exercise intensity, etc.; for example, the movement period, the frequency of movement within each movement period, the duration of each movement, and the like are selected from the movement time.
The dynamic data comprises initial movement force detection data and body indexes in the movement process, wherein the initial movement force detection data can be obtained in the following mode:
based on the exercise theory support of the sport expert, by means of fitness equipment such as equipment combined with a treadmill, a sport bicycle, a barbell and the like and/or by means of set exercise actions such as standing long jump, flat plate support and the like, exercise evaluation items are generated, real-time data in the process of implementing the exercise evaluation items by a target user are obtained, and the second real-time data are called as second real-time data for convenient literacy,
in the implementation process of the exercise evaluation item, body indexes in the exercise process, such as the maximum oxygen uptake in the exercise process of a target user, the heart rate in the exercise process and the like, are collected through sensor equipment, and the body indexes are called as second body indexes for convenience of literacy; the normative of the movement action of the target user is detected by collecting image data in the movement process and utilizing an intelligent detection algorithm, and the movement capacity of the target user is comprehensively evaluated by combining static data and a second body index, so that initial movement capacity detection data is obtained.
Performing data analysis and mining by using the acquired first data to acquire fitness characteristic attributes of the target user, wherein the fitness characteristic attributes include and are not limited to: basic information of the target user, fitness purpose, exercise level, exercise preference, exercise time and other dimensions. Each dimension may in turn include several sub-dimensions.
Referring to fig. 4, fig. 4 is a schematic diagram of the fitness feature attributes of the target user. Each dimension can be considered through a fusion result obtained by weighting evaluation data of the sub-dimensions included in the dimension. Wherein the content of the first and second substances,
determining basic characteristics of a target user based on the basic information;
determining a moving object characteristic of the target user based on the fitness objective,
determining a motion level characteristic of the target user based on the dynamic data,
determining a sports interest characteristic of the target user based on the preferred sports item,
determining a motion temporal characteristic of the target user based on the motion habit,
and weighting and fusing the basic characteristics, the moving target characteristics, the moving level characteristics, the moving interest characteristics and the moving time characteristics into fitness characteristic attributes.
The fitness characteristic attribute describes the motion characteristics of the target user from multiple dimensions, corresponding to the motion portrayal of the target user.
The fitness characteristic attribute can be obtained through the trained first machine learning model, wherein the first data is input data of the machine learning model, and the fitness characteristic attribute is output of the machine learning model.
Step 202, generating a matched fitness plan for the target user based on the fitness characteristic attributes, wherein the fitness plan at least comprises one of exercise intensity, exercise type, exercise action and exercise frequency,
as one example, a fitness plan is determined based on each dimension in the fitness characteristics attributes.
Referring to FIG. 5, FIG. 5 is a schematic diagram of generating a matched fitness plan for a target user based on fitness characteristics attributes. Outputting a motion plan adaptive to the target user based on the motion level, the motion purpose, the appeal (such as strength enhancement, muscle building, shaping, physical ability and the like) and the preference motion mode of the target user and combined with the theoretical support of a kinematics expert; the exercise plan is developed, and the exercise plan of the target user can be automatically updated along with the change of the fitness characteristic attribute. For example, in FIG. 5, the exercise intensity is matched based on the basic information in the fitness feature attributes; matching the type of the movement based on the movement purpose and/or the movement preference; matching the motion actions based on the motion level; and matching the exercise frequency based on the exercise time, and weighting and fusing the exercise intensity, the exercise type, the exercise action and the exercise frequency into the fitness plan.
It should be understood that the motion of FIG. 5 may be one planned motion program or a combination of multiple planned motion programs. In the case of multiple planned sports, each planned sports may have its own exercise intensity, exercise frequency, and exercise motion, and in this case, the fitness plan may be a weighted fusion of the planned sports.
The fitness plan may be determined according to each feature dimension in the fitness feature attributes by matching using a trained second machine learning model, wherein the features in the fitness feature attributes are input data, and the matched fitness plan is output data of the second machine learning model.
Step 203, in the process of implementing the fitness plan by the target user, acquiring real-time data associated with the fitness of the target user, wherein the real-time data is convenient for running and becomes first real-time data, and real-time detection and guidance are carried out on the fitness process by utilizing the real-time data.
As one example, the acquisition of fitness real-time data is triggered in response to an action performed by a targeted user fitness plan. For example, the target user turns on a camera or an exercise machine sensor to perform the acquisition of the first real-time data. The first real-time data includes a first body index during an exercise included in the fitness plan and first athletic performance image data.
Referring to fig. 6, fig. 6 is a schematic diagram of a first motion process image data for real-time normative detection and guidance.
And when the target user implements according to the fitness plan, triggering and generating a standard motion process of the motion items contained in the fitness plan, and displaying the standard motion process in real time.
The method comprises the steps of acquiring real-time motion data of a target user by using an image acquisition device, and capturing a real-time motion track and a motion action, wherein the motion track can be a 3D motion track or a 2D motion track.
Based on the captured motion trail and motion action, performing motion normative detection on the motion process, namely detecting the normative of the motion trail and the motion action; as an example, with a visual camera with AI computing power, normative during exercise such as leg lift angle, stride length and frequency, rope jump times and height, etc. may be detected.
Comparing the captured motion trail and motion action with the standard motion trail and the standard motion action, if the difference between the two is within a set difference threshold value, judging that the current motion action is correct and meets the motion specification, outputting a first prompt, for example, encouraging, otherwise, judging that the current motion action is wrong and does not meet the motion specification, and outputting a second prompt, for example, prompting the error and giving a prompt of the correct motion action.
The execution is repeated until the exercise item is finished, wherein the condition of the exercise item finishing can be the exercise duration and the achieved physiological data, such as the heart rate and/or the consumed heat.
In order to increase interactivity and interestingness in the implementation process of the fitness plan, real-time 3D modeling is carried out by utilizing a standard motion process so as to construct a selectable first virtual object which forms mapping with the standard motion process, the first virtual object can be correspondingly a standard motion image, an intelligent anthropomorphic coach is analogized, compared with a real-person coach, the posture of the intelligent anthropomorphic coach is more standard, meanwhile, the current exercise part can be labeled in real time, and the motion attribute in the motion can also be displayed in real time in a data form;
the real-time 3D modeling can be carried out by utilizing the motion process data of the target user to construct a selectable second virtual object which forms mapping with the motion process of the target user, the second virtual object is displayed in real time, and the body index can be optionally displayed according to the first body index; for example, by performing 3D real-time modeling on the posture and the movement process track of the target user to generate a second virtual object for providing an intuitive visual reference, the virtual object can show the current movement track, physiological data (such as heart rate and heat consumed) and movement data (such as motion completion speed) of the user in real time.
The method can also utilize the motion process data of other users except the target user to carry out real-time modeling so as to construct an optional third virtual object which forms mapping with the motion process of the other users, display the third virtual object in real time, and optionally display body indexes according to the first body indexes of the other users; for example, the motion process data of a synchronous sporter performing the same motion with the target user synchronously is utilized to perform 3D real-time modeling on the posture and the motion trajectory of the synchronous sporter, and generate a virtual object, so as to improve the sociability in the motion process.
An optional fourth virtual object may also be constructed that corresponds to the fitness plan.
In addition, according to the execution process of the fitness plan, the virtual object can interact with the target user by utilizing a machine understanding model of natural language; by utilizing the virtual object and the game scene engine, scene modes such as forests, cities, mountains and the like are synchronously displayed, and the auxiliary motion process is more interesting.
Preferably, the virtual object may be a digital person with a digitized human avatar displayed by means of an on-screen display device, or may be a non-digital person with a digitized non-human avatar, such as a self-drawn avatar.
In order to increase the safety of the motion scene in the implementation process of the fitness plan, the safety detection of the motion scene can be performed, for example, whether target objects such as pets, children, old people and the like invade or not is detected based on image data acquired by the visual AI camera, if so, a warning prompt is output in a linkage manner, and the fitness equipment is stopped or prohibited from being started, so that the safety protection effect is achieved.
In order to increase the personal safety of the target user in the implementation process of the fitness plan, personal safety detection can be carried out, for example, whether the first body index does not accord with a set index threshold value is judged based on the first body index, if so, unsafe factors of the target user are judged, an alarm prompt is output in a linkage mode, and the fitness equipment is shut down or is prohibited from being started.
The effect of safe movement is favorably achieved through the safety monitoring in the movement process.
And step 204, performing fitness detection by using the first real-time data, adjusting fitness characteristic attributes according to the fitness detection data, and determining a next fitness plan matched with the target user based on the adjusted fitness characteristic attributes.
As an example, when the fitness plan is completed, the exercise force detection data is obtained by using the operation normative detection data obtained in the exercise process of the fitness plan, and combining the first body index and the static data, if the difference between the exercise force detection data and the last exercise force detection data reaches the set threshold, the exercise level in the fitness characteristic attribute of the target user is updated, so that the fitness characteristic attribute is updated along with the completion of each fitness plan, the current fitness plan is adjusted according to the updated exercise level, and the next fitness plan is obtained.
Referring to fig. 7, fig. 7 is a schematic diagram of detecting a motion force by using first real-time data. For example, parameters such as heart rate, Body Mass Index (BMI), Basal Metabolic Rate (BMR), exercise rating, and the like are acquired from the acquired first exercise process image data and the sensor data.
As another example, when the number of times of completion of the fitness plan reaches the set first time threshold, and/or the number of times of updating the exercise level reaches the set second time threshold, and/or when the current exercise level reaches the set level threshold, the exercise level characteristics in the fitness characteristic attributes of the target user may be updated by using the historical exercise force detection data in combination with the historical first body indicators and the historical static data, so that the fitness characteristic attributes are updated along with the change of the exercise level, and the fitness plan is adjusted according to the updated exercise level, which is equivalent to customizing the fitness prescription for the target user.
The embodiment utilizes the exercise force detection data to evaluate the exercise level of the target user, uses the exercise level as one of the fitness characteristic attributes of the target user to describe and update the exercise portrait of the target user, and adjusts the fitness plan based on the update of the exercise portrait, so that the fitness process of the target user forms closed-loop feedback of fitness plan setting, fitness plan completion, evaluation and fitness plan adjustment, thereby realizing the dynamic adjustment of the fitness plan, setting the adjustment frequency according to the requirement, improving the intelligence of fitness guidance, and being beneficial to the effect of scientific fitness.
Referring to fig. 8, fig. 8 is a schematic view of an apparatus for performing exercise instruction. The device includes:
a fitness characteristic attribute acquisition module for acquiring fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise a movement force characteristic attribute for representing movement capacity,
a fitness plan matching module for determining a matched fitness plan for the target user based on the fitness characteristic attributes,
a process data acquisition module for acquiring first real-time data associated with the fitness of the target user during the exercise plan, wherein the first real-time data at least comprises real-time data for athletic performance detection,
the body-building detection module is used for carrying out body-building detection by utilizing the first real-time data, wherein the body-building detection at least comprises movement force detection data,
and the adjusting module is used for adjusting the fitness characteristic attribute at least according to the movement force detection data and determining a next matched fitness plan for the target user based on the adjusted fitness characteristic attribute.
Wherein the content of the first and second substances,
the fitness characteristic attribute acquisition module is configured to acquire first data related to fitness of a target user in an initial establishment stage of target user data, wherein the first data comprises: static data for characterizing static feature attributes of the target user, and dynamic data for characterizing motor force feature attributes of the target user;
using the first data, a fitness characteristic attribute of the target user is determined.
The fitness characteristic attribute acquisition module is further configured to acquire the static data through feedback information of a target user and/or data collection of a sensor, wherein the static data comprises at least one of basic information, fitness objectives, preferred sports, sports habits,
generating a motion evaluation item by means of the fitness equipment and/or the set motion action, acquiring second real-time data of a target user in the implementation process of the motion evaluation item, wherein the second real-time data comprises a second body index in the motion process and second motion process image data,
detecting the normativity of the movement process of the target user by using an intelligent detection algorithm based on the second movement process image data,
evaluating the athletic ability of the target user by combining the static data and the second physical index to obtain initial athletic ability detection data,
determining the initial movement detection data and the second body index as dynamic data.
The fitness characteristic attribute acquisition module is further configured to determine basic characteristics of the target user based on the basic information;
determining a moving object characteristic of the target user based on the fitness objective,
determining a motion level characteristic of the target user based on the dynamic data,
determining a sports interest characteristic of the target user based on the preferred sports item,
determining a motion temporal characteristic of the target user based on the motion habit,
and weighting and fusing the basic characteristics, the moving target characteristics, the moving level characteristics, the moving interest characteristics and the moving time characteristics into fitness characteristic attributes.
The fitness plan matching module is configured to, based on the base characteristics, determine a matched exercise intensity,
determining the matched motion type according to the motion interest characteristic and/or the motion object characteristic,
determining a matching motion action according to the motion level characteristics,
determining the matched motion frequency according to the motion time characteristics,
and weighting and fusing the exercise intensity, the exercise type, the exercise action and the exercise frequency into the fitness plan.
The fitness detection module is configured to detect the normativity of the movement process of the target user by utilizing an intelligent detection algorithm based on the first movement process image data to obtain movement normativity detection data,
determining athletic performance test data based on the athletic normative test data in combination with the first body index and the static data.
The process data acquisition module is configured to generate a standard motion process according to the motion actions included in the fitness plan and display the standard motion process in real time; and acquiring the motion process data of the target user based on the first motion process image data, comparing the acquired motion process data of the target user with the standard motion process data, and performing motion guidance according to the comparison data.
The device also comprises any one of the following modules:
the first safety detection module is used for carrying out safety detection on a motion scene based on the first motion process image data;
the second safety detection module is used for carrying out personal safety detection based on the first body index;
the interaction module is used for establishing voice interaction by utilizing a machine understanding model of natural language according to the execution process of the fitness plan;
the virtual object module is used for carrying out real-time modeling by utilizing the standard motion process so as to construct a selectable first virtual object which forms mapping with the standard motion process and display the first virtual object and the current exercise part and/or motion attribute in real time;
the system comprises a target user, a first virtual object and a second virtual object, wherein the target user is used for carrying out real-time modeling by utilizing the motion process data of the target user so as to construct a selectable second virtual object which forms mapping with the motion process of the target user, displaying the second virtual object in real time, and optionally displaying body indexes according to first body indexes of other users;
the system comprises a real-time modeling module, a display module and a display module, wherein the real-time modeling module is used for utilizing the motion process data of other users except the target user to construct a selectable third virtual object which forms mapping with the motion process of the other users, displaying the third virtual object in real time, and optionally displaying body indexes according to first body indexes of the other users;
for constructing an optional fourth virtual object corresponding to the fitness plan.
Wherein the content of the first and second substances,
the first safety detection module is configured to detect whether an invaded target object exists or not based on the first motion process image data, if so, the motion scene is determined to be unsafe, an alarm prompt is output in a linkage mode, and the fitness equipment is shut down or is prohibited from being started;
the first safety detection module is configured to judge whether the first body index does not accord with a set index threshold value, if so, judge that the target user has unsafe factors, output an alarm prompt in a linkage mode, and shut down the fitness equipment or forbid starting the fitness equipment.
The adjustment module is configured to: triggering the adjustment of the fitness characteristic attribute if any one or any combination of the following conditions is met:
ending any fitness plan;
the number of times of finishing the fitness plan reaches a set first time threshold;
the number of times of motion level updating reaches a set second time threshold value;
the motion level reaches a set level threshold;
and also,
under the condition that the adjustment of the fitness characteristic attribute is triggered after the fitness plan is finished, at least adjusting the motion level characteristic in the fitness characteristic attribute according to the motion detection data and the first body index after the fitness plan is finished and by combining the static data;
and under the condition that the adjustment of the fitness characteristic attribute is triggered under other conditions except the end of the fitness plan, at least adjusting the motion level characteristic in the fitness characteristic attribute according to the historical motion force detection data and by combining the historical first body index and the historical static data.
Referring to fig. 9, fig. 9 is a schematic diagram of a system for implementing exercise guidance, the system comprising:
a collecting device for collecting real-time data in the process of movement and an electronic device provided with a client,
a communication link is established between the acquisition device and the electronic equipment for data transmission,
the electronic device comprises a memory storing a computer program and a processor configured to execute the steps of the computer program to implement any of the exercise guidance implementing methods.
The system further comprises an exercise machine and a control system,
the acquisition device comprises an image acquisition device for acquiring moving image data in the movement process and/or a sensing device for acquiring body indexes in the movement process.
As one example, the image capture device is integrated with an electronic device, and when the exercise machine is integrated with a sensor for capturing real-time data during the exercise of the exercise machine, a communication link is established between the exercise machine and the electronic device for data transmission.
As another example, the exercise machine may be a virtual exercise machine, i.e., a digitized exercise machine, which may be implemented on the electronic device side; the acquisition device may also be integrated in the electronic device.
The electronic device may be a smart terminal.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored in the storage medium, and when being executed by a processor, the computer program realizes the steps of any one of the fitness guidance realization methods.
For the device/network side device/storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method embodiment.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (14)
1. A method for implementing fitness guidance, the method comprising: on the side of the client side,
obtaining fitness characteristic attributes of the target user, wherein the fitness characteristic attributes at least comprise movement force characteristic attributes used for representing movement capacity,
determining a matching fitness plan for the target user based on the fitness characteristic attributes,
obtaining first real-time data associated with the target user for fitness while the fitness plan is being implemented, wherein the first real-time data includes at least real-time data for athletic performance detection,
performing fitness detection by using the first real-time data, wherein the fitness detection at least comprises exercise force detection data,
and adjusting the fitness characteristic attribute at least according to the movement force detection data, and determining a next fitness plan matched with the target user based on the adjusted fitness characteristic attribute.
2. The implementation method of claim 1, wherein the obtaining of fitness feature attributes of the target user comprises:
in an initial establishment stage of target user data, acquiring first data related to fitness of a target user, wherein the first data comprises: static data for characterizing static feature attributes of the target user, and dynamic data for characterizing motor force feature attributes of the target user;
using the first data, a fitness characteristic attribute of the target user is determined.
3. The method of claim 2, wherein obtaining first data associated with the target user for the workout comprises:
obtaining the static data through feedback information of a target user and/or data collection of a sensor, wherein the static data comprises at least one of basic information, fitness objectives, preferred sports, sports habits,
generating a motion evaluation item by means of the fitness equipment and/or the set motion action, acquiring second real-time data of a target user in the implementation process of the motion evaluation item, wherein the second real-time data comprises a second body index in the motion process and second motion process image data,
detecting the normative of the movement process of the target user by using an intelligent detection algorithm based on the second movement process image data,
evaluating the athletic ability of the target user by combining the static data and the second physical index to obtain initial athletic ability detection data,
determining the initial movement detection data and the second body index as dynamic data.
4. The implementation method of claim 3, wherein the determining fitness feature attributes of the target user using the first data comprises:
determining basic characteristics of a target user based on the basic information;
determining a moving object characteristic of the target user based on the fitness objective,
determining a motion level characteristic of the target user based on the dynamic data,
determining a sports interest characteristic of the target user based on the preferred sports item,
determining a motion temporal characteristic of the target user based on the motion habit,
and weighting and fusing the basic characteristics, the moving target characteristics, the moving level characteristics, the moving interest characteristics and the moving time characteristics into fitness characteristic attributes.
5. The implementation method of claim 4, wherein determining a matching fitness plan for a target user based on fitness feature attributes comprises:
and determining the matched motion intensity according to the basic characteristics,
determining the matched motion type according to the motion interest characteristics and/or the motion object characteristics,
determining a matched motion action according to the motion level characteristics,
determining the matched motion frequency according to the motion time characteristics,
and weighting and fusing the exercise intensity, the exercise type, the exercise action and the exercise frequency into the fitness plan.
6. The method of claim 5, wherein the first real-time data includes first body indicators during the exercise included in the fitness plan and first athletic performance image data,
the utilizing the first real-time data to perform fitness detection comprises the following steps:
detecting the normativity of the movement process of the target user by utilizing an intelligent detection algorithm based on the first movement process image data to obtain movement normativity detection data,
determining athletic performance test data based on the athletic normative test data in combination with the first body index and the static data.
7. The method of claim 6, wherein prior to obtaining the first real-time data associated with the target user for exercising, further comprising:
generating a standard motion process according to the motion actions included in the fitness plan, and displaying the standard motion process in real time;
after the obtaining the first real-time data associated with the fitness of the target user, the method further comprises:
acquiring the motion process data of the target user based on the first motion process image data,
and comparing the acquired movement process data of the target user with standard movement process data, and performing movement guidance according to the comparison data.
8. The method of claim 6, wherein the exercise program is executed by at least one of:
based on the first motion process image data, carrying out motion scene safety detection;
performing personal safety detection based on the first body index;
establishing voice interaction by utilizing a machine understanding model of natural language according to the execution process of the fitness plan;
performing real-time modeling by utilizing the standard motion process to construct a selectable first virtual object which forms mapping with the standard motion process, and displaying the first virtual object, the current exercise part and/or motion attributes in real time;
carrying out real-time modeling by utilizing the motion process data of the target user to construct an optional second virtual object which forms mapping with the motion process of the target user, displaying the second virtual object in real time, and optionally displaying body indexes according to the first body indexes of other users;
modeling in real time by using the motion process data of other users except the target user to construct a selectable third virtual object which forms mapping with the motion process of the other users, displaying the third virtual object in real time, and optionally displaying body indexes according to the first body indexes of the other users;
and constructing an optional fourth virtual object corresponding to the fitness plan.
9. The method of claim 8, wherein the performing motion scene security detection based on the first motion process image data comprises:
detecting whether an invaded target object exists or not based on the first motion process image data, if so, judging that the motion scene is unsafe, outputting an alarm prompt in a linkage manner, and stopping the fitness equipment or forbidding starting the fitness equipment;
the detecting of personal safety based on the first body index includes:
and judging whether the first body index does not accord with a set index threshold value, if so, judging that unsafe factors exist in the target user, outputting an alarm prompt in a linkage manner, and shutting down the fitness equipment or forbidding starting the fitness equipment.
10. The method of claim 6, wherein said adjusting said fitness feature attribute based on at least the motion detection data comprises:
triggering the adjustment of the fitness characteristic attribute if any one or any combination of the following conditions is met:
ending any fitness plan;
the number of times of finishing the fitness plan reaches a set first time threshold;
the number of times of motion level updating reaches a set second time threshold value;
the motion level reaches a set level threshold;
and the number of the first and second electrodes,
under the condition that the adjustment of the fitness characteristic attribute is triggered after the fitness plan is finished, at least adjusting the motion level characteristic in the fitness characteristic attribute according to the motion detection data and the first body index after the fitness plan is finished and by combining the static data;
and under the condition that the adjustment of the fitness characteristic attribute is triggered under other conditions except the end of the fitness plan, at least adjusting the motion level characteristic in the fitness characteristic attribute according to the historical motion force detection data and by combining the historical first body index and the historical static data.
11. An apparatus for performing fitness guidance, the apparatus comprising:
a fitness characteristic attribute acquisition module for acquiring fitness characteristic attributes of a target user, wherein the fitness characteristic attributes at least comprise a movement force characteristic attribute for representing movement capacity,
a fitness plan matching module for determining a matched fitness plan for the target user based on the fitness characteristic attributes,
a process data acquisition module for acquiring first real-time data associated with the fitness of the target user during the exercise plan, wherein the first real-time data at least comprises real-time data for athletic performance detection,
the body-building detection module is used for carrying out body-building detection by utilizing the first real-time data, wherein the body-building detection at least comprises movement force detection data,
and the adjusting module is used for adjusting the fitness characteristic attribute at least according to the movement force detection data and determining a next matched fitness plan for the target user based on the adjusted fitness characteristic attribute.
12. A system for realizing body-building guidance is characterized by comprising a collecting device for collecting real-time data in the movement process and electronic equipment provided with a client, wherein a communication link is established between the collecting device and the electronic equipment;
the electronic device comprises a memory storing a computer program and a processor configured to execute the steps of the computer program to implement the method of any one of claims 1 to 10.
13. The system of claim 12, further comprising an exercise machine, wherein a communication link is established between the exercise machine having the sensor and the electronic device;
the acquisition device comprises an image acquisition device for acquiring moving image data in the movement process and/or a sensing device for acquiring body indexes in the movement process.
14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of a method for carrying out a fitness guide according to any one of claims 1 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115862807A (en) * | 2022-09-02 | 2023-03-28 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
| CN117766098A (en) * | 2024-02-21 | 2024-03-26 | 江苏森讯达智能科技有限公司 | Body-building optimization training method and system based on virtual reality technology |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140248595A1 (en) * | 2013-03-04 | 2014-09-04 | Get Into FIT LLC | Online interactive fitness trainer |
| CN107551521A (en) * | 2017-08-17 | 2018-01-09 | 广州视源电子科技股份有限公司 | Exercise guide method and device, smart machine and storage medium |
| CN107731275A (en) * | 2017-09-25 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | A kind of method and system of dynamic adjustment health exercising plan |
| CN108364674A (en) * | 2018-02-22 | 2018-08-03 | 国家体育总局体育科学研究所 | A kind of intelligent body-building guidance method and system using exercise prescription |
| CN108766524A (en) * | 2018-05-22 | 2018-11-06 | 深圳市零度智控科技有限公司 | Exercise guide method, apparatus and computer readable storage medium |
| CN110478862A (en) * | 2019-05-07 | 2019-11-22 | 深圳市云康创新网络科技有限公司 | A kind of exercise guide system and its guidance method |
| CN210933642U (en) * | 2019-05-07 | 2020-07-07 | 深圳市天玖隆科技有限公司 | Body-building guidance system |
| CN111986775A (en) * | 2020-08-03 | 2020-11-24 | 深圳追一科技有限公司 | Body-building coach guiding method and device for digital person, electronic equipment and storage medium |
| CN114283920A (en) * | 2021-11-15 | 2022-04-05 | 黄山米骑网络科技有限公司 | Body-building guidance system based on data intelligent monitoring |
-
2022
- 2022-07-21 CN CN202210856689.2A patent/CN114937485A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140248595A1 (en) * | 2013-03-04 | 2014-09-04 | Get Into FIT LLC | Online interactive fitness trainer |
| CN107551521A (en) * | 2017-08-17 | 2018-01-09 | 广州视源电子科技股份有限公司 | Exercise guide method and device, smart machine and storage medium |
| CN107731275A (en) * | 2017-09-25 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | A kind of method and system of dynamic adjustment health exercising plan |
| CN108364674A (en) * | 2018-02-22 | 2018-08-03 | 国家体育总局体育科学研究所 | A kind of intelligent body-building guidance method and system using exercise prescription |
| CN108766524A (en) * | 2018-05-22 | 2018-11-06 | 深圳市零度智控科技有限公司 | Exercise guide method, apparatus and computer readable storage medium |
| CN110478862A (en) * | 2019-05-07 | 2019-11-22 | 深圳市云康创新网络科技有限公司 | A kind of exercise guide system and its guidance method |
| CN210933642U (en) * | 2019-05-07 | 2020-07-07 | 深圳市天玖隆科技有限公司 | Body-building guidance system |
| CN111986775A (en) * | 2020-08-03 | 2020-11-24 | 深圳追一科技有限公司 | Body-building coach guiding method and device for digital person, electronic equipment and storage medium |
| CN114283920A (en) * | 2021-11-15 | 2022-04-05 | 黄山米骑网络科技有限公司 | Body-building guidance system based on data intelligent monitoring |
Non-Patent Citations (1)
| Title |
|---|
| 赵汉理等: "个性化定制的虚拟健身系统设计与实现", 《中国图象图形学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115862807A (en) * | 2022-09-02 | 2023-03-28 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
| CN115862807B (en) * | 2022-09-02 | 2024-02-02 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
| CN117766098A (en) * | 2024-02-21 | 2024-03-26 | 江苏森讯达智能科技有限公司 | Body-building optimization training method and system based on virtual reality technology |
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