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.
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