کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6900157 | 1446487 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multi-sensor Golf Swing Classification Using Deep CNN
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
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چکیده انگلیسی
In recent years smart sport equipments have achieved great success in professional and amateur sports, as well as body sensory systems; now discovering interesting knowledge in the surge of data from those embedded sensors used in sports is necessary and the focus of our research. In this paper, we investigate golf swing data classification method based on deep convolutional neural network (deep CNN) fed with multi-sensor golf swing signals. Our smart golf club integrates two orthogonally affixed strain gage sensors, 3-axis accelerometer and 3-axis gyroscope, and collects real-world golf swing data from professional and amateur golf players. Furthermore we explore the performance of our well-trained CNN-based classifier and evaluate it on the real-world test set in terms of common indicators including accuracy, precision-recall, and F1-score. Experiments and corresponding results show that our CNN-based model can satisfy the requirement of accuracy of golf swing classification, and outperforms support vector machine (SVM) method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 129, 2018, Pages 59-65
Journal: Procedia Computer Science - Volume 129, 2018, Pages 59-65
نویسندگان
Libin Jiao, Hao Wu, Rongfang Bie, Anton Umek, Anton Kos,