Large-scale parallel aerobic capacity grouping method

A large-scale, capable technology used in medical science, sports accessories, sensors, etc. to improve accuracy and speed

Pending Publication Date: 2021-12-03
东南数字经济发展研究院
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the shortcomings of the prior art, solves the problem of how to quickly and efficiently cluster large-scale multivariate non-iso-aerobic test sequences, and proposes an efficient and parallelizable large-scale aerobic capacity Clustering method

Method used

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  • Large-scale parallel aerobic capacity grouping method
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  • Large-scale parallel aerobic capacity grouping method

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Embodiment Construction

[0026] The efficient and parallel large-scale aerobic capacity grouping method proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] refer to figure 2 , a large-scale aerobic capacity grouping task needs to perform the following steps in the calculation:

[0028] (1) The aerobic capacity test sequence set D=[AS 1 , AS 2 ,...AS n ] loaded into memory. AS i (i=1,...,n) is a two-dimensional aerobic capacity test sequence including heart rate and speed, denoted as AS i =i , hs i >, where ss i Represents a sequence of velocity values, hs i Represents a sequence of heart rate values. The aerobic capacity test sequence is collected by students wearing professional sports bracelets during the 22-minute aerobic endurance run test (the rules of the 22-minute aerobic endurance run test are shown in the table below).

[0029]

[0030]

[0031] (2) Perform data bucketing on the aerobic capacity t...

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Abstract

The invention discloses a large-scale parallel aerobic capacity grouping method. The method comprises the following steps: 1) loading an aerobic capacity test sequence data set; 2) performing data bucket dividing; 3) parallelizing each data bucket, wherein the processing steps specifically comprise: 3.1) preprocessing sequences in the buckets; 3.2) carrying out re-representation on the preprocessed sequence; 4) performing normalization processing on the re-representation sequence obtained by parallel processing to obtain a clustering sample set; and 5) carrying out clustering and clustering on the clustering sample set. According to the method, an algorithm suitable for clustering large-scale multivariable unequal-length aerobic capacity test sequences is realized, and large-scale aerobic capacity grouping can be quickly and effectively realized.

Description

technical field [0001] The present invention relates to an efficient, parallelizable method suitable for grouping aerobic capacity of large-scale student groups. Background technique [0002] With the maturity and wide application of wearable devices, large-scale collection of human physiological health data through wearable devices has become an effective and feasible method. By wearing a sports heart rate bracelet, people's sports physiological data can be collected on a large scale, and the sports physiological data of large-scale people contains huge value. Through the reasonable analysis and mining of large-scale exercise physiological data, the individual's health status can be obtained from the data, so as to customize a reasonable exercise plan, which is an important means to prevent emergencies during exercise and improve personal health. Exercise physiological data is essentially a time series data, and the common mining tasks for time series are: anomaly detectio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/024A61B5/11A61B5/00A63B71/06
CPCA61B5/02438A61B5/1121A61B5/1118A61B5/681A61B5/7264A63B71/0619A63B2220/30A63B2230/06
Inventor 杨良怀匡东伟
Owner 东南数字经济发展研究院
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