دانلود مقاله انگلیسی رایگان:یک روش جدید برای تشخیص وضعیت سلامت رباط صلیبی خلفی ورزشکاران با استفاده از الکترومیوگرافی سطح و شبکه عصبی کانونال عمقی - 2019
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  • A new method of diagnosing athletes anterior cruciate ligament health status using surface electromyography and deep convolutional neural network A new method of diagnosing athletes anterior cruciate ligament health status using surface electromyography and deep convolutional neural network
    A new method of diagnosing athletes anterior cruciate ligament health status using surface electromyography and deep convolutional neural network

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    A new method of diagnosing athletes anterior cruciate ligament health status using surface electromyography and deep convolutional neural network


    ترجمه فارسی عنوان مقاله:

    یک روش جدید برای تشخیص وضعیت سلامت رباط صلیبی خلفی ورزشکاران با استفاده از الکترومیوگرافی سطح و شبکه عصبی کانونال عمقی


    منبع:

    Sciencedirect - Elsevier - Integrative Medicine Research, Uncorrected proof: doi:10:1016/j:bbe:2019:05:009


    نویسنده:

    Mehran Hatamzadeh Q1 a, Reza Hassannejad a,*, Ali Sharifnezhad


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 1388 کیلوبایت

    قیمت: رایگان


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