با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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A new method of diagnosing athletes anterior cruciate ligament health status using surface electromyography and deep convolutional neural network
یک روش جدید برای تشخیص وضعیت سلامت رباط صلیبی خلفی ورزشکاران با استفاده از الکترومیوگرافی سطح و شبکه عصبی کانونال عمقی-2019 Anterior cruciate ligament (ACL) injury is one of the most common injuries in high-demand
sports. Due to long-term treatment of this injury, diagnosing recovery of ACL becomes
important, particularly six months postoperatively. The purpose of this research is to
provide a cost-effective and intelligent method to diagnose ACLs health status. For this
purpose, 11 healthy and 27 ACL-injured subjects have been selected. In the proposed
method, the athlete performs a single-leg landing protocol and surface electromyographic
signals (EMG) are taken from eight lower limb muscles. Then, time–frequency distributions
of EMG signals in each landing are calculated as an image, using pseudo Wigner–Ville
distribution (PWVD), which are the inputs of a deep convolutional neural network (DCNN).
By time–frequency analysis, it has been made clear that any change in ACLs health status
causes changes in the extent of energy spread in PWVD, distribution volume, frequency
content, damping rate and the peak value of EMG signals. In this research, a new relationship
between ACLs health status and lower limb muscles activity is introduced through moni-
toring of PWVD images. The result indicates that the designed expert system is able to
diagnose ACLs health status with 95.8% accuracy. In this non-invasive method, PWVD
images of EMG signals are chosen as the inputs of DCNN, instead of MRI images, which, in
addition to their high accuracy in diagnosing, are safer and much cheaper. The presented
method can play an important role in assessing the recovery process, six months postoperatively
and after that. Keywords: ACLs health status | Single-leg landing | Surface electromyography | Pseudo Wigner–Ville distribution | Deep convolutional neural networks |
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