Grouping method of students' aerobic capacity driven by big data

A big data-driven, student-driven technology, applied in informatics, medical informatics, health index calculation, etc., can solve the problems of large amount of data, clustering algorithms that cannot perform iterative grouping operations, and cannot be loaded at the same time at one time. The effect of accuracy

Active Publication Date: 2021-05-04
ZHEJIANG UNIV OF TECH
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Problems solved by technology

Due to the large amount of data, it cannot be loaded into the memory for processing at one time, and the traditional clustering algorithm cannot perform iterative clustering operations

Method used

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  • Grouping method of students' aerobic capacity driven by big data
  • Grouping method of students' aerobic capacity driven by big data
  • Grouping method of students' aerobic capacity driven by big data

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Embodiment Construction

[0046] The big data-driven student physique grouping method of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0047] The present invention solves the problem of grouping and clustering of students' aerobic capacity data. Among them, each student's aerobic capacity data is a numerical multivariate time series type, and the length of each aerobic capacity data is non-equal length. The representation of a piece of aerobic capacity data is shown in Table 1.

[0048] Table 1 A representation table of aerobic capacity data

[0049]

[0050] In the existing method, it is impossible to accurately compare the similarity of non-equal-length matrix data directly. The present invention proposes a two-stage clustering algorithm to solve the clustering problem of this type of data. The first stage adopts the HMM-based student's Oxygen capacity model generation algorithm, the second stage adopts hierarchical clustering alg...

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Abstract

Big data-driven aerobic ability grouping method for students, which includes the following steps: 1) Use the aerobic ability data as the observation data of the Hidden Markov Model (HMM), and initialize the HMM parameters; 2) Train the HMM to solve the model parameters, according to HMM, HMM prediction based on the observation sequence to obtain the state transition sequence; 3) Using the state transition sequence to calculate the aerobic capacity model of each student; 4) Using the student’s aerobic capacity model, using the KL distance to calculate the individual student’s The similarity between students is obtained, and the similarity matrix between students is obtained, and the student's physique is grouped by using hierarchical clustering. The present invention proposes a method for grouping students' aerobic ability driven by big data, which can group students according to their aerobic ability, realize personalized portraits and grouping of students' physique, and can be used for individualized physical exercise and training.

Description

technical field [0001] The present invention relates to a method for grouping students' aerobic capacity driven by big data, in particular to a two-stage grouping method for students' aerobic capacity data under the background of big data, and in particular to aerobic capacity with multidimensional time series and non-equal length in time dimension Data, a non-equal-length multivariate time series clustering method is proposed. Background technique [0002] Since 1985, my country has conducted six nationwide surveys on the physical health of adolescents. Surveys show that the physical fitness of Chinese teenagers continues to decline. Due to long-term lack of exercise, overweight and obesity are serious, the incidence of myopia continues to increase, and poor blood pressure regulation is relatively common. Some common diseases of middle-aged and elderly people (such as coronary heart disease, high blood pressure, etc.) have often appeared in some teenagers in recent years. ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/30
Inventor 杨良怀王海龙柳乔凡周君来周雷李海鹏范玉雷龚卫华
Owner ZHEJIANG UNIV OF TECH
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