CN111028911A - Motion data analysis method and system based on big data - Google Patents
Motion data analysis method and system based on big data Download PDFInfo
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Abstract
The invention discloses a motion data analysis method and system based on big data, wherein the method comprises the following steps: acquiring continuous motion data generated by the fitness behavior of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter; and analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree. The method is suitable for real-time processing and feedback of mass motion data, and can judge whether the fitness behavior of the user is standard or not so as to further provide corresponding guidance suggestions.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a motion data analysis method and system based on big data.
Background
Under the existing exercise mode, when a user trains, especially for a novice user, the condition that exercise motions are incorrect or nonstandard often occurs, and if the user trains according to the incorrect or nonstandard motions for a long time, the user is not beneficial to progress, and is easy to hurt, so that great potential safety hazards exist.
In the prior art, in order to assist a user in professional training, a coach generally gives professional motion guidance to the user, so that the user is helped to correct the motion, and injuries caused by wrong motion of the user are avoided. However, the method corrects the wrong actions by means of manual guidance, so that the cost is high, the convenience is poor, and the detailed analysis and guidance of the movement of all occasions cannot be performed; meanwhile, in the process of an actual sports match, the actual sports data of the user cannot be obtained for analysis, and the improvement of the sports competitive level of the user is not facilitated.
Disclosure of Invention
The invention aims to provide a motion data analysis method and system based on big data, which are suitable for real-time processing and feedback of mass motion data and can judge whether the fitness behavior of a user is standard or not so as to further provide corresponding guidance suggestions.
In order to solve the above technical problem, an embodiment of the present invention provides a motion data analysis method based on big data, including:
acquiring continuous motion data generated by the fitness behavior of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter;
and analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
Preferably, the big data-based motion data analysis method further includes:
and carrying out visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
Preferably, the big data-based motion data analysis method further includes:
extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model, and identifying the fitness behavior to obtain exercise behavior parameters; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
Preferably, the big data-based motion data analysis method further includes:
and determining guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
Wherein the motion data comprises at least one of:
limb movement track, force, amplitude, speed, heart rate and blood pressure.
An embodiment of the present invention further provides a motion data analysis system based on big data, including:
the data acquisition unit is used for acquiring continuous motion data generated by the fitness behavior of the user and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter;
and the data analysis unit is used for analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion quantity parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
Preferably, the big data based motion data analysis system further includes:
and the visualization processing unit is used for performing visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
Preferably, the big data based motion data analysis system further includes: the body-building behavior identification unit is used for extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model and identifying the body-building behavior to obtain an exercise behavior parameter; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
Preferably, the big data based motion data analysis system further includes: the data analysis unit is also used for determining the guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
Wherein the motion data comprises at least one of:
limb movement track, force, amplitude, speed, heart rate and blood pressure.
The embodiment of the invention has the following beneficial effects:
the invention discloses a motion data analysis method and system based on big data, wherein the method comprises the following steps: acquiring continuous motion data generated by the fitness behavior of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter; and analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree. The method is suitable for real-time processing and feedback of mass motion data, and can judge whether the fitness behavior of the user is standard or not so as to further provide corresponding guidance suggestions.
Drawings
FIG. 1 is a flow chart of a big data based motion data analysis method in an embodiment of the present invention;
fig. 2 is a flow chart of a big data based motion data analysis method in a preferred embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Please refer to fig. 1.
An embodiment of the present invention provides a motion data analysis method based on big data, including:
s101, acquiring continuous motion data generated by fitness behaviors of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion amount parameter and a motion duration parameter.
And S102, analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
Wherein the motion data comprises at least one of: limb movement track, force, amplitude, speed, heart rate and blood pressure.
In a preferred embodiment, the big data based motion data analysis method further includes:
and carrying out visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
Please refer to fig. 2.
In a preferred embodiment, the big data based motion data analysis method further includes:
s103, extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model, and identifying the fitness behavior to obtain exercise behavior parameters; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
The big data-based motion data analysis method further comprises the following steps:
and S104, determining guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
An embodiment of the present invention further provides a motion data analysis system based on big data, including:
the data acquisition unit is used for acquiring continuous motion data generated by the fitness behavior of the user and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter;
and the data analysis unit is used for analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion quantity parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
Preferably, the big data based motion data analysis system further includes:
and the visualization processing unit is used for performing visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
Preferably, the big data based motion data analysis system further includes: the body-building behavior identification unit is used for extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model and identifying the body-building behavior to obtain an exercise behavior parameter; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
Preferably, the big data based motion data analysis system further includes: the data analysis unit is also used for determining the guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
Wherein the motion data comprises at least one of:
limb movement track, force, amplitude, speed, heart rate and blood pressure.
The invention discloses a motion data analysis method and a system based on big data, wherein the method comprises the following steps: acquiring continuous motion data generated by the fitness behavior of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter; and analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree. The method is suitable for real-time processing and feedback of mass motion data, and can judge whether the fitness behavior of the user is standard or not so as to further provide corresponding guidance suggestions.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A motion data analysis method based on big data is characterized by comprising the following steps:
acquiring continuous motion data generated by the fitness behavior of a user, and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter;
and analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion amount parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
2. The big-data-based motion data analysis method according to claim 1, further comprising:
and carrying out visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
3. The big-data-based motion data analysis method according to claim 1, further comprising:
extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model, and identifying the fitness behavior to obtain exercise behavior parameters; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
4. The big-data-based motion data analysis method according to claim 3, further comprising:
and determining guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
5. The big data based motion data analysis method according to any of claims 1 to 4, wherein the motion data comprises at least one of:
limb movement track, force, amplitude, speed, heart rate and blood pressure.
6. A big-data-based motion data analysis system, comprising:
the data acquisition unit is used for acquiring continuous motion data generated by the fitness behavior of the user and preprocessing the motion data; the preprocessed motion data at least comprises one of a motion intensity parameter, a motion quantity parameter and a motion duration parameter;
and the data analysis unit is used for analyzing and comparing the motion data with preset standard motion data, judging whether the motion intensity parameter, the motion quantity parameter or the motion duration parameter of the motion data are matched with the preset standard motion data, if not, determining that the motion of the body-building behavior of the user is not marked, and outputting the nonstandard degree.
7. The big-data based motion data analysis system of claim 6, further comprising:
and the visualization processing unit is used for performing visualization processing on the motion data to obtain three-dimensional visualization data corresponding to the motion data, and displaying the three-dimensional visualization data through a display.
8. The big-data based motion data analysis system of claim 6, further comprising:
the body-building behavior identification unit is used for extracting the exercise intensity characteristic and the exercise rhythm characteristic of the preprocessed exercise data, establishing an identification model and identifying the body-building behavior to obtain an exercise behavior parameter; the motion behavior parameters at least comprise a steady-state motion intensity parameter and a steady-state motion rhythm parameter; steady state refers to the state of motion of the current user's inertia.
9. The big-data based motion data analysis system of claim 8, further comprising:
the data analysis unit is also used for determining the guidance suggestion information of the fitness behavior according to the exercise behavior parameters and the nonstandard degree.
10. The big data based motion data analysis system of any of claims 6 to 9, wherein the motion data comprises at least one of:
limb movement track, force, amplitude, speed, heart rate and blood pressure.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114141332A (en) * | 2021-12-07 | 2022-03-04 | 贝塔智能科技(北京)有限公司 | User running exercise data analysis and exercise suggestion algorithm |
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