Exercise prescription generation method based on improved collaborative filtering ItemCF algorithm

An exercise prescription and collaborative filtering technology, which is applied in computing, electrical digital data processing, special data processing applications, etc.

Pending Publication Date: 2020-11-13
XIAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a motion prescription generation method based on the improved collaborative filtering ItemCF algor

Method used

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  • Exercise prescription generation method based on improved collaborative filtering ItemCF algorithm
  • Exercise prescription generation method based on improved collaborative filtering ItemCF algorithm
  • Exercise prescription generation method based on improved collaborative filtering ItemCF algorithm

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Experimental program
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Embodiment 1

[0106] Combined with the implementation below, from step 1 to step 5, an example is introduced to calculate the fitness of sports items to user input based on the improved collaborative filtering ItemCF algorithm:

[0107] Step 1.1,

[0108] Establish an exercise prescription exercise item database. Before using the invention to generate an exercise prescription for the first time, it is necessary to collect enough exercise prescription exercise items. The collection content of each exercise item includes the name of the exercise item, exercise prescription type, exercise effect, action object, and exercise intensity. , exercise duration, and exercise frequency; for example, the collection content of cycling sports is: the name of the sport is cycling, the type of exercise prescription is aerobic, and the exercise effects include fat loss and weight loss, exercise balance, decompression, and strength enhancement. For the whole body, the exercise intensity is 143bmp, the exerci...

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Abstract

The invention discloses an exercise prescription generation method based on an improved collaborative filtering ItemCF algorithm. The exercise prescription generation method comprises the following steps: 1, collecting an exercise project, dividing the exercise project into an exercise effect attribute, an action object attribute and an exercise condition attribute, quantifying the exercise effectattribute and the action object attribute to establish a motion project quantification database; 2, calculating the distribution of motion effect attribute in a database; 3, acquiring user input data, and establishing an ideal motion quantification model according to the motion data; 4, according to the ideal motion quantification model, calculating the fitness of all motions in the motion quantification database to user input by using an improved collaborative filtering ItemCF algorithm; 5, screening an exercise item with the highest fitness, setting exercise intensity, exercise frequency and exercise duration, and generating an exercise prescription; and 6, collecting new user input data, and repeating the step 3 to the step 5 from the step 3 to obtain a new exercise prescription. According to the invention, the problem in the prior art that the exercise prescription generation result does not accord with the exercise ability and exercise conditions of the user is solved.

Description

technical field [0001] The invention belongs to the technical field of motion prescription generation, and relates to a motion prescription generation method based on an improved collaborative filtering ItemCF algorithm. Background technique [0002] People generally like to enhance their physical fitness through sports to maintain physical and mental health. Now, with the enrichment of material civilization, people have more requirements for sports. Exercise prescription is an effective means for exercise to intervene in health. Setting exercise programs based on scientific methods can ensure exercise effectiveness and avoid exercise injuries. Computer technology to generate exercise prescriptions is a research field that has emerged to facilitate people to obtain exercise prescriptions suitable for their own needs and conditions. It mainly uses methods such as similarity recommendation, decision tree, and data mining to realize the generation or recommendation of exercise ...

Claims

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Application Information

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IPC IPC(8): G16H20/30G06F16/9536
CPCG16H20/30G06F16/9536
Inventor 王磊练兴杨
Owner XIAN UNIV OF TECH
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