已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Construction of a Classification Model for Teacher and Student Behavior in Physical Education Classrooms –Based on Multimodal Data

数学教育 体育 心理学 计算机科学 教育学
作者
Zhao Yan,Bingyan Yu
出处
期刊:Applied mathematics and nonlinear sciences [De Gruyter]
卷期号:9 (1) 被引量:1
标识
DOI:10.2478/amns-2024-1818
摘要

Abstract The analysis of teachers' and students' behaviors in physical education classrooms is an important way to improve the quality of physical education teaching and teaching methods, which helps teachers to check the gaps and improve the teaching level. In this paper, for the problems of data differences between multiple modalities and the conflict between feature extraction modules of different modalities, we designed a dual-stream framework HRformer algorithm based on Transformer, which unifies the skeletal modalities and video modalities in the algorithm. The relationship between skeletal and video modalities is modeled using the self-attention mechanism, and the matching and fusion of skeletal features and video data is performed to construct a behavior recognition model for teachers and students in the sports classroom based on multimodal data. Then, the model is compared with mainstream networks on the dataset to verify its performance. To conduct model application and example analysis, a university collects data on physical education classroom teachers and students for a semester. It is found that the multimodal model in this paper has a classification F1 value of 95.61%, 93.19%, and 93.74% for the three types of behavior recognition, namely, skill training (ST), game activity (GA), and rest, respectively, which are higher than the two methods of single skeletal modality and video modality. The model has the highest recognition accuracy of 97.12% and 98.15% for Game Activity (GA). Based on real physical education classroom data, the practical application of the model in physical education teaching classrooms in this paper is fruitful, and the results of behavioral recognition classification are in line with the design expectation. This study develops an effective method for classifying teacher and student behaviors in a physical education classroom. It provides a useful exploration for the integration and innovation of physical education teaching and information technology.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
syyw2021发布了新的文献求助10
2秒前
2秒前
2秒前
鲤鱼安青完成签到 ,获得积分10
2秒前
北海西贝发布了新的文献求助10
4秒前
宇宇完成签到 ,获得积分10
4秒前
Heidi完成签到 ,获得积分10
4秒前
哈哈哈关注了科研通微信公众号
5秒前
li完成签到 ,获得积分10
7秒前
7秒前
8秒前
雪白发布了新的文献求助10
9秒前
轩辕远航完成签到 ,获得积分10
10秒前
陌路完成签到 ,获得积分10
11秒前
明理囧完成签到 ,获得积分10
11秒前
北海西贝完成签到,获得积分10
11秒前
Evan完成签到 ,获得积分10
12秒前
14秒前
HJJHJH发布了新的文献求助100
14秒前
附姜完成签到 ,获得积分10
16秒前
泡芙1207发布了新的文献求助10
18秒前
等待的问安完成签到,获得积分10
19秒前
奔跑石小猛完成签到,获得积分10
19秒前
复杂的可乐完成签到 ,获得积分10
21秒前
21秒前
wmuer完成签到 ,获得积分10
21秒前
21秒前
Easypass完成签到 ,获得积分10
21秒前
酷波er应助平凡的世界采纳,获得10
22秒前
喜悦元正完成签到,获得积分20
23秒前
马敏完成签到 ,获得积分10
23秒前
yzthk完成签到 ,获得积分10
24秒前
bkagyin应助少言采纳,获得10
24秒前
呜呼啦呼完成签到,获得积分10
24秒前
wulixin完成签到,获得积分10
26秒前
打打应助北雨采纳,获得10
26秒前
子翱完成签到 ,获得积分10
26秒前
喜悦元正发布了新的文献求助20
26秒前
科研铁人完成签到 ,获得积分10
26秒前
1111chen完成签到 ,获得积分10
26秒前
高分求助中
Continuum Thermodynamics and Material Modelling 4000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
Novel synthetic routes for multiple bond formation between Si, Ge, and Sn and the d- and p-block elements 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3516200
求助须知:如何正确求助?哪些是违规求助? 3098475
关于积分的说明 9239612
捐赠科研通 2793430
什么是DOI,文献DOI怎么找? 1533082
邀请新用户注册赠送积分活动 712550
科研通“疑难数据库(出版商)”最低求助积分说明 707329