طبقه بندی نوسان گلف با شبکه عصبی کانولوشن عمیق
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طبقه بندی نوسان گلف با شبکه عصبی کانولوشن عمیق

عنوان فارسی مقاله: طبقه بندی نوسان گلف با شبکه عصبی کانولوشن عمیق متعدد
عنوان انگلیسی مقاله: Golf swing classification with multiple deep convolutional neural networks
مجله/کنفرانس: مجله بین المللی شبکه های حسگر توزیع شده - International Journal Of Distributed Sensor Networks
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده
کلمات کلیدی فارسی: دستگاه هوشمند، تحلیل اطلاعات گلف، طبقه بندی، شبکه عصبی کانولوشن، یادگیری عمیق
کلمات کلیدی انگلیسی: Smart device، golf data analysis، classification، convolutional neural network، deep learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: DOAJ - Master Journal List - JCR - Scopus
شناسه دیجیتال (DOI): https://doi.org/10.1177/1550147718802186
دانشگاه: College of Information Science and Technology - Beijing Normal University - China
ناشر: سیج - Sage
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 1/460 در سال 2017
شاخص H_index: 31 در سال 2019
شاخص SJR: 0/255 در سال 2017
شناسه ISSN: 1550-1477
شاخص Quartile (چارک): Q2 در سال 2017
فرمت مقاله انگلیسی: Pdf
تعداد صفحات مقاله انگلیسی: 17
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E10825
فهرست انگلیسی مطالب

Abstract


Introduction


Related work


Data collection


Methodology


Experiments and results


Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


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.


Introduction


Advances in technology and data science are changing the way of practicing and training in recreational, amateur, and professional sports. The collection of sports performance data has become easier and more reliable with the development of miniature, lightweight sensors, sensor networks, and communication technologies. The key issue now is how to analyze the large amounts of (streaming) data from the above-mentioned wearable devices. The processing requirements for sensor signals and data have become more demanding, both in volume and time constraints. Sensors in sports can be attached to the user or an integral part of the (smart) sports equipment. Systems and applications in sports that are using wearable sensor data can be designed for a great variety of uses, from monitoring particular movements of an individual to overseeing the complete action in a group sports match. According to the intended use, the complexity of the design varies considerably. Our plans are to develop biofeedback applications, particularly in biomechanical feedback systems, that would use sensors’ data for providing the concurrent feedback to the user.1 According to Sigrist et al.,2 proper motor learning can be accelerated by the identification and prevention (interruption) of incorrectly performed actions. Our aim is to design and implement a real-time system that would notify the user about the incorrect action during the action itself or immediately after each period of a periodic activity.

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