- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
The use of smart sports equipment and body sensory systems supervising daily sports training is gradually emerging in professional and amateur sports; however, the problem of processing large amounts of data from sensors used in sport and discovering constructive knowledge is a novel topic and the focus of our research. In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. In particular, four convolutional neural classifiers are customized: ‘‘GolfVanillaCNN’’ with the convolutional layers, ‘‘GolfVGG’’ with the stacked convolutional layers, ‘‘GolfInception’’ with the multi-scale convolutional layers, and ‘‘GolfResNet’’ with the residual learning. Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network–based classifiers from two perspectives: classification implementations and sensor combinations. Besides, we further evaluate the performance of these four classifiers in terms of classification accuracy, precision–recall curves, and F1 scores. These common classification indicators illustrate that our convolutional neural network–based classifiers can basically group the golf swing predefined by the combination of shapes and golf players correctly and outperform support vector machine method representing traditional classification methods.
In this article, we investigate golf swing data classification methods based on varieties of classifiers of deep CNNs fed with multi-sensor sequences. The CNNbased classifiers are adequate to correctly group the multi-channel golf swing data labeled by the hybrid categories from different golf players and shapes and quantitatively outperform SVM classifier in terms of widely accepted evaluation indicators including accuracy, precision–recall indicators and curves, and F1 scores on the preserved test set. Some conclusions are proclaimed again here. The indicators including accuracy, precision– recall curves, and F1 scores can quantitatively demonstrate that CNN-based classifiers can reach the acceptable accuracy in the golf swing classification tasks and outperform the SVM classifier. The consistent performance of accuracy among sensors can demonstrate that signals from even one single sensor can be adequate in identifying shapes of golf swings, while the vulnerable gyroscope is easy to be intervened and may not individually produce distinguishable signals. It has been illustrated that CNN-based classifiers are basically tolerant with the time translation and other plausibly existed noise imported initially since the consistency of indicators is observed in the 10-fold cross-validation and the test phase.