Real-Time Classification of Sports Movement Using Adaptive Clustering

Li, Kin Fun; Sevcenco, Ana-Maria; Takano, Kosuke

doi:10.1109/cisis.2012.213

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…9,23,25 The recognized tennis stroke types were few in number and/or the recognition accuracies were less than 90%. 14,23 O ´Conaire et al 9 employed six wearable devices to collect data, but only recognized three types of tennis strokes with an average accuracy of 93.44%. Liu 23 used four wearable devices with a sampling frequency of 560 Hz, classifying different strokes with an average accuracy of 79%.…”

Section: Discussionmentioning

confidence: 99%

“…When it comes to applying MEMS sensors in tennis, most studies used tri-axial accelerometers, tri-axial gyroscopes, and tri-axial magnetometers to collect motion data. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Notably, these studies usually employed multiple wearable devices with a sampling frequency greater than 100 Hz. 9,[17][18][19][23][24][25] These wearable devices were placed around major joints of the human body and/or inside the racket's handle connected to a computer or smartphone via Bluetooth technology to transfer the collected data.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have started to employ machine learning techniques to process MEMS sensor data and classify tennis strokes. 9,14,[17][18][19]21,23,[25][26][27][28] For example, Whiteside et al 17 used a 500 Hz sensor combined with discriminant analysis, Support Vector Machine, K-nearest neighbor, Classification Tree, Random Forest, and Neural Network algorithms to recognize and classify four stroke types with the highest accuracy of 93.2%. Liu 23 used four wearable devices (560 Hz) combined with SVM to recognize and classify eight types of tennis strokes with an accuracy of 79%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part P:

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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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