利用数据流分析技术进行有氧运动学习

IF 1.5 Q2 EDUCATION & EDUCATIONAL RESEARCH
Qiuping Peng, Ningfei Wei
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引用次数: 0

摘要

在高校体育课程改革的背景下,培养学生的体育锻炼兴趣,促进学生终身体育锻炼已成为关键。健美操作为体育教学不可或缺的组成部分,在实现这些目标的过程中发挥着关键作用。然而,现有的数据流分析技术缺乏整合性,限制了其利用各种来源信息的能力。为解决这一问题,本文提出了一种健美操教学模型,该模型利用少量学习技术进行数据流分析。该模型结合了基于度量学习的标签特征网络,增强了分析多尺度特征和类内标签特征的能力。对比分析表明,与传统的图像特征组合分类器模型相比,该模型的准确率提高了 8.12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aerobics Teaching With Few-Shot Learning Technology for Data Flow Analysis
In the context of college physical education curriculum reform, fostering students' interest and promoting lifelong physical exercise have become crucial. Aerobics, an integral component of physical education, plays a key role in achieving these objectives. However, existing data flow analysis technologies lack integration, limiting their ability to leverage information from various sources. To address this issue, this paper proposes an aerobics teaching model utilizing few-shot learning technology for data flow analysis. The model incorporates a label feature network based on metric learning, enhancing its ability to analyze multi-scale features and label features within classes. Comparative analysis demonstrates an 8.12% improvement in accuracy compared to traditional image feature combined classifier models.
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来源期刊
CiteScore
4.20
自引率
10.00%
发文量
26
期刊介绍: IJICTE publishes contributions from all disciplines of information technology education. In particular, the journal supports multidisciplinary research in the following areas: •Acceptable use policies and fair use laws •Administrative applications of information technology education •Corporate information technology training •Data-driven decision making and strategic technology planning •Educational/ training software evaluation •Effective planning, marketing, management and leadership of technology education •Impact of technology in society and related equity issues
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