Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

支持向量机 人工智能 过度拟合 朴素贝叶斯分类器 计算机科学 规范化(社会学) 机器学习 模式识别(心理学) 分类器(UML) 可穿戴计算机 交叉验证 人工神经网络 人类学 社会学 嵌入式系统
作者
Mingyue Wu,Ran Wang,Yang Hu,Mengjiao Fan,Yufan Wang,Yanchun Li,Shengyuan Wu
出处
期刊:Proceedings Of The Institution Of Mechanical Engineers, Part P: Journal Of Sports Engineering And Technology [SAGE Publishing]
卷期号:237 (4): 271-282 被引量:11
标识
DOI:10.1177/17543371211050312
摘要

This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min–Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms’ classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min–Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
刚刚
1秒前
圆蓬蓬发布了新的文献求助10
2秒前
ZQP发布了新的文献求助10
2秒前
Meloqi发布了新的文献求助10
2秒前
文静的翠彤完成签到 ,获得积分10
2秒前
3秒前
WLWLW驳回了乐乐应助
3秒前
奋斗的忆南完成签到,获得积分10
3秒前
小邢一定行完成签到,获得积分10
4秒前
4秒前
4秒前
顾矜应助ZQP采纳,获得10
5秒前
吃的了细糠的山猪完成签到,获得积分10
6秒前
ldy完成签到,获得积分10
6秒前
6秒前
深味i完成签到,获得积分10
7秒前
祝您发财完成签到,获得积分10
7秒前
8秒前
tomorrow完成签到 ,获得积分10
8秒前
ldy发布了新的文献求助10
8秒前
苹果信封发布了新的文献求助10
9秒前
友好驳发布了新的文献求助10
9秒前
乐乐应助LALball采纳,获得10
10秒前
10秒前
iiing_7完成签到,获得积分10
10秒前
迷路的鞅发布了新的文献求助10
11秒前
ZQP完成签到,获得积分10
12秒前
华仔应助勤恳的夏之采纳,获得10
12秒前
tex发布了新的文献求助10
12秒前
科研通AI2S应助内向寒云采纳,获得10
14秒前
昵称发布了新的文献求助10
15秒前
nnnn完成签到,获得积分10
17秒前
17秒前
迷路的鞅完成签到,获得积分10
18秒前
Lucas应助友好驳采纳,获得10
19秒前
完美世界应助圆蓬蓬采纳,获得10
19秒前
19秒前
20秒前
田様应助zh采纳,获得10
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3975722
求助须知:如何正确求助?哪些是违规求助? 3520056
关于积分的说明 11200719
捐赠科研通 3256455
什么是DOI,文献DOI怎么找? 1798271
邀请新用户注册赠送积分活动 877490
科研通“疑难数据库(出版商)”最低求助积分说明 806390