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
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.
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