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儿童运动表现数据集。

Kids motor performances datasets.

作者信息

Maliki Ahmad Bisyri Husin Musawi, Abdullah Mohamad Razali, Nadzmi Ahmad, Zainoddin Mohamad Amirur Rafiqi, Puspitasari Intan Meily, Jibril Nur Faizatul Amira, Nawi Nur Amirah, Mat-Rasid Siti Musliha, Musa Rabiu Muazu, Suhaili Zarizal, Kamarudin Noor Aishah, Ali Syed Kamaruzaman Syed

机构信息

East Coast Environment Research Institute, 21300 University of Sultan Zainal Abidin, Gong Badak Campus, Terengganu, Malaysia.

Faculty of Applied Science Social University of Sultan Zainal Abidin, 21300 University of Sultan Zainal Abidin, Gong Badak Campus, Terengganu, Malaysia.

出版信息

Data Brief. 2020 Nov 30;34:106582. doi: 10.1016/j.dib.2020.106582. eCollection 2021 Feb.

DOI:10.1016/j.dib.2020.106582
PMID:33354597
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7744643/
Abstract

These datasets described the data of the Motor Performance Index for 7 years old kids in Malaysia based on Malaysia's physical fitness test SEGAK. This database has been designed and created with data analysis to create the index from the factor and variable of the test and the test was conducted in the majority of the national primary school in Malaysia. Gender, state of origin, and residential location of the school were the factors used to categorize the participant of the test. The factor of age, weight, height, body mass index (BMI), power, flexibility, coordination, and speed were used for the measurement to relate with the participant's physical fitness. Kids Motor Performances Index data can be reused for talent identification in sport talent scout and to create a baseline for kid's biology growth specifically in gross motor skills and cognitive growth measurement.

摘要

这些数据集描述了马来西亚7岁儿童基于马来西亚体能测试SEGAK的运动表现指数数据。该数据库是通过数据分析设计和创建的,以便根据测试的因素和变量创建该指数,并且该测试在马来西亚的大多数国立小学进行。性别、原籍州和学校的居住地点是用于对测试参与者进行分类的因素。年龄、体重、身高、体重指数(BMI)、力量、柔韧性、协调性和速度等因素用于测量,以关联参与者的身体素质。儿童运动表现指数数据可重新用于体育人才选拔中的人才识别,并为儿童的生物生长,特别是在大肌肉运动技能和认知生长测量方面创建基线。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/aa0664731a82/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/1141ffb491e9/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/ca68fc57e5fd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/9c30d3c0a5ec/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/aa0664731a82/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/1141ffb491e9/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/ca68fc57e5fd/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/9c30d3c0a5ec/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf2/7744643/aa0664731a82/gr4.jpg

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本文引用的文献

1
A machine learning approach of predicting high potential archers by means of physical fitness indicators.一种通过体能指标预测高潜力射箭运动员的机器学习方法。
PLoS One. 2019 Jan 3;14(1):e0209638. doi: 10.1371/journal.pone.0209638. eCollection 2019.
2
The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.基于体能和运动能力变量识别高潜力射箭运动员:一种支持向量机方法。
Hum Mov Sci. 2018 Feb;57:184-193. doi: 10.1016/j.humov.2017.12.008. Epub 2017 Dec 14.